Sudan Academy of Science (SAS) Council of atomic energy Medical Physics Program

Batch (6)

Assessment of Image Quality during CT Examinations using Image Processing

A Thesis submitted for Partial Fulfillment M .Sc Degree in Medical Physics

By: Mossab Edrees Abdelazeez Mohamed

Supervisor: Dr. SuhaibMohamedsalih AhamedAlameen Assessment of Image Quality during CT Examinations using Image Processing

By Mussab Edrees Abdelazeez Mohamed

Examination committee

Title Name Signature

Supervisor Dr.SuhaibMohamedAlameen

External Dr. Nadia Omer Alatta examiner

January 2018 االية

قال تعالى:

(قالو سبحانك العلم لنا اال ما علمتنا انك انت العليم الحكيم )

االيت (23) سورة البقرة

صدق هلال العظيم

I Dedication

:؛;:؛;:؛;:؛ح

TO MY parents :؛;:؛;:؛;:؛ح

TO My Brothers :؛;:؛;:؛;:؛ح

TO My Friends :؛;:؛;:؛;:؛;:؛ح

And Class Mate

:؛;:؛;:؛;:؛;:؛;:؛ح

I Dedicate This Researches

II Acknowledgements

Firstly: I would like to express my gratitude and appreciation to mercifully God(Allah).

Also: I would like to express my sincere gratitude to my supervisor Dr: SuhaibAlameen for his useful, keens, efforts and help at any time.

I would like to express my thanks also to my family.

III Contents

Title Page I االية Dedication II Acknowledgements III Contents IV List of Tables VI List of figures VII List of symbols and abbreviations VIII Abstract IX X المستخلص CHAPTER 1 INTRODUCTION 1.1 Background 1 1.2Image processing 3 1.3 ImageJ software 3 1.4Problem of the Study 4 1.5Motivation 4 1.6 Objectives of the Research 5 1.6.1 General Objectives 5 1.6.2 Specific Objectives 5 1.7 Overview of study 5 CHAPTER 2 LITERATURE REVIEW 2.1 Image processing 6 2.2 Medical Image Processing 7 2.3 Medical Image 8 2.4 Computed Tomography 8 2.5 The quality of a digital image 12 2.5.1 Spatial resolution and pixel size 12 2.5.2 Brightness resolution 14 2.5.3 content 15 2.5.3.1 Type of noise 17 2.6 Image Denoising Techniques 21 2.6.1 Linear filters 21 2.6.2 Non- linear filters 26 2.7 Image Quality Assessment Methods 29 2.7.1 Signal-to-Noise Ratio (SNR) 30 2.7.2 Mean Square Error (MSE) 31

IV 2.7.3 Peak Signal to Noise Ratio (PSNR) 31 2.7.4 Structural Similarity Index Metric (SSIM) 32 2.8 Previous Related Works 34 CHAPTER 3 METHODOLOGY 3.1 Material 37 3.2 Place and time of study 38 3.3 Study sample 38 3.4Study variables 38 3.5 Test images format 38 3.6 Analysis of data 38 3.7 ImageJ software 38 CHAPTER 4 RESULTS 4.1 Results 40 CHAPTER 5 5.1 Discussions 47 5.2 Conclusions 50 5.3 Recommendations 51 5.4 References 52 APPENDICES 55

V LIST OF TABLES

Table NO Content Page NO 4.1 MSE, PSNR and SSIM after applied Mean, Gaussian, 43 and Minimum filters to abdomen CT image. 4.2 Averages values of MSE, PSNR, and SSIM after applied 43 Mean Gaussian, Median and Minimum filters to 100 abdomen CT images. 4.3 MSE, PSNR, SSIM after applied Mean, Gaussian, Median 46 and Minimum filters to brain CT image. 4.4 Averages values of MSE, PSNR, and SSIM after applied 47 Mean, Gaussian, Median and Minimum filters to 100 brain CT images.

VI LIST OF FIGURES

Fig NO Content Page NO 2.1 Schematic representation of a first-generation CT scanner 11 2.2 Grids of various sizes superimposed on the point spread 14 function image. 2.3 Test pattern image corrupted by and 18 its histogram 2.4 Sanfrancisco image corrupted by Poisson noise. 20 2.5 Test pattern image corrupted by salt-and pepper noise and 20 15 its histogram. 2.6 Denoising by mean filter after applied different masks 22 2.7 Mercy image corrupted by salt and pepper noise and 26 denoising by Gaussian filter. 2.8 X-ray image of circuit board corrupted by salt-and pepper 28 noise and denoised with Mean filter. 3.1 Toshiba Computed Tomography (CT), 64 slice. 39 3.2 Control of Computed Tomography (CT). 39 3.3 ImageJ software window. 41 3.4 Medical image in ImageJ software and analyses window. 41 4.1 Different linear and Non-Linear Filters Applied for abdomen 42 CT scan Image. 4.2 Histogram of MSE and several filters Applied for Abdomen 44 CT images. 4.3 Histogram of PSNR and several filters Applied for Abdomen 44 CT images. 4.4 Histogram of SSIM and several filters Applied for Abdomen 45 CT images. 4.5 Different linear and Non-Linear Filters Applied for Brain CT 46 scan Image. 4.6 Histogram of MSE and several filters Applied for brain 47 CT images. 4.7 Histogram of PSNR and several filters Applied for brain 48 CT images. 4.8 Histogram of SSIM and several filters Applied for brain 48 CT images.

VII LIST OF SYMBOLS AND ABBREVIATIONS

dB V Variance SD Standard deviation k Edge Magnitude Parameter IT Information Technology CT Computed Tomography MRI Magnetic Resonance Imaging US Ultrasound SPECT Single Photon Emission Computed Tomography PET Positron Emission Tomography SNR Signal-to-Noise Ratio MSE Mean Square Error PSNR Peak-Signal-to-Noise Ratio SSIM Structural Similarity Index Metric PDF Probability Density Function AWGN Additive White Gaussian Noise HVS Human Visual System NIH National Institute of Health TIFF Tagged Image File Format PNG Portable Network Graphic GIF Graphics Interchange Format JPEG Joint Photographic Experts Group BMP Bitmap image file DICOM Digital Imaging and Communication in Medicine FITS Flexible Image Transport linear attenuation coefficient أأ PSF Point spread function MTF Modulation transfer function ADC Analog-to-Digital Converter

VIII Abstract

The images corrupted by noise during acquisition and transmission process, so removing of noise from the images still a challenging problem for researchers.

Assessment of image quality during CT examinations; the data collected from Royal Care International Hospital Radiology Department, in period from February to September 2017, used different techniques that removed the noise fromall original images, ((100) abdomen and (100)brain CT images).ImageJ software used for analyzed images and applying filters to remove noise from CT images. These filters are Mean filter, Gaussian filter, and Minimum filter. Image quality after denoising measured in this study based on Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and the Structural Similarity Index Metric (SSIM). The results show that the images quality parameters becomes higher and different, Median filter showed high level of image quality parameter that given by Peak Signal-to-Noise Ratio(PSNR) and structural similarity index metrics (SSIM),and it consider as a better filter for removing the noise from all other filters applied.

The study concluded the noise removed by the different filters that applied to the CT images result in high quality of images enhanced and maintaining the important details of the images.

IX المستخلص

الصور الطبية تتاثر بالضوضاء نتيجة لتكوينها و معالجتها ، و تعتبر عملية ازالة الضوضاء من اكبر التحديات للباحثين

. تقييم جودة الصور اثناء اختبارات االشعة المقطعية، تم تجميع البيانات من مستشفي رويفاك كيفر العفالم لف اليتفرة

من لبراير الف بفبتمبر 7102،وتفم بفتخدا تقنيفات مختليفة الزالفة الضوضفاء مفن كفر الصفور اال فلية 011 فورة للراس و011 دورة للبطن، ، بالمعتخداء برنامحImageJ^ العور. الفرشيحات المستخدمة شه مرشح الوبفط،

مرش، غاوس ، مرش، الوبي و المرش، اال غر.

جودة العورة بعفد زالفة الضوضفاء المقفابفة لف شفرا الدرابفة بنفاء علف معفامتت جفودة العفورا وشف متوبف الخطف

التربيعفف ، نسففبة اةشففارة لفف الضوضففاء ، ومهشففر التشففابي الهيكلفف المتففري . وامهففرت النتففا ا معففامتت جففودة

العور تعب، اعل ومختلية، وامهرت ا المرش، المتوب لديي مستوى عاك من معامتت جودة العورة الت تعطيها

نسبة اةشارة ل الضوضاء ومقفاييس مهشر التشابي الهيكل ، وتعتبر ا المرش، المتوب الضر مرش، ةزالة الضوضاء

من بين جميع المرشحات األخرى الت تم ابتخدامها .

وخلعفا الدرابفة لفف ا الضوضفاء التفف تمفا زالتهفا مفن خبفر المرشففحات المختليفة التفف بقفا علفف العفور اال فلية

ادت ال جودة عالية من العور المحسنة والحيفاظ عل تيفا ير شامة من العور.

X CHAPTER ONE INTRODUCTION 1.1 Background

Medical information composing of clinical data, images, and other physiological signals, has become an essential part of a patient's care, whether during the screening, diagnostic stage or treatment phase. Over the past three decades, rapid developments in Information Technology (IT) and medical instrumentation have facilitated the development of digital medical imaging. This development has mainly concerned Computed Tomography (CT), Magnetic Resonance Imaging (MRI), different digital radiological processes of vascular, cardiovascular and contrast imaging, mammography, diagnostic ultrasound imaging, nuclear medical imaging with Single Photon Emission Computed Tomography (SPECT), and Positron Emission Tomography (PET). All these processes have produced ever- increasing quantities of images. These images are different from typical photographic images, primarily because they reveal internal anatomy as opposed to an image of the surface. Specificities of medical images in natural monochromatic or color images, the pixel intensity of the image corresponds to the reflection coefficient of natural light[1].

Medical imaging systems detect different physical signals arising from patients and produce images. An imaging in tow-dimensional function where x and y are spatial coordinate, and the amplitude of at any pair of coordinates (x, y) is called the intensity or gray level of image at that point. When x, y and amplitude values of are all finite and discrete quantities, we call the image a digital image. Digital image is composed of finite number of elements, each of which has a particular location and value. These elements are referred to as image elements or pixels. CT imaging is the primary digital technique for imaging the chest, lungs, abdomen and bones due to its ability to combine fast data acquisition and high resolution[2].

- 1 - Noise is the unwanted, random (stochastic) fluctuations in an image. The principal sources of noise in a digital imaging system are photon (or quantum) noise, which arises from the discrete nature of electromagnetic radiation and its interactions with matter, and electronic noise in detectors or amplifiers.Medical staff requires a best medical images quality for achieving efficient and fast diagnosis. Indeed the obtained images from different medical acquisition techniques are blurred and usually corrupted by noise. Corruption of noisy images with blur makes it poor for visual analysis. For these reasons, many studies has been done and gave as resulting output several denoising methods which can be categorized in two classes. In the first category the original image is transformed in frequency or domain then removing noise methods are applied as: Hard or Soft thresolding coefficients. In the second category the unwanted noise is directly suppressed in spatial domain[3]. Image de-noising is one of the most significant tasks in image processing, analysis and image processing applications. Medical imaging is one of the emerging application areas where the image de-noising plays a vital role. Image denoising is an essential pre-requisite, especially in CT images, which an important modality in medical imaging. Image de-noising is a procedure in aiming at the removal of noise. Noise removal is essential in medical imaging applications in order to enhance and recover fine details that may be hidden in the data, Noisy image is a corrupted binary image needed by the filtering technique to restore the image,and this operation is done by throwing some pixels from the image and replaced with random gray values. In some images, the noise will not be a big matter if it is in the low level, but when it has high level of noise it will be unwanted noise, several filtering methods are used to remove unwanted noise for example Average filtering, Gaussian filtering, Log filtering, Median filtering, Wiener filtering, and so on[3].

- 2 - 1.2 Image processing

Image processing is processing of images using mathematical operations by using any form of for which the input is an image, a series of images, or a video, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image . Most image-processing techniques involve treating the image as a two­ dimensional signal and applying standard signal-processing techniques to it. Images are also processed as three-dimensional signals where the third-dimension being time or the z-axis. [4]

Image processing usually refers to digital image processing, but optical and analog image processing also are possible. This article is about general techniques that apply to all of them. The acquisition of images (producing the input image in the first place) is referred to as imaging. [5]

1.3 ImageJ software:

Prior to the release of ImageJ in 1997, a similar free software image analysis program known as NIH Image had been developed in Object Pascal for Macintosh computers running pre-OS X operating systems. Further development of this code continues in the form of Image SXM, a variant tailored for physical research of scanning microscope images. A Windows version - ported by Scion Corporation (now defunct), so-called Scion Image for Windows - was also developed.

ImageJ is an open source image processing program designed for scientific multidimensional images. ImageJ is highly extensible, with thousands of plugins and macros for performing a wide variety of tasks, and a strong, established user base. This wiki documents all aspects of the ImageJ ecosystem. ImageJ can display, edit, analyze, process, save, and print 8-bit color and grayscale, 16-bit integer, and 32-bit floating point images. It can read many image file formats,

- 3 - including TIFF, PNG, GIF, JPEG, BMP, DICOM, and FITS, as well as raw formats. ImageJ supports image stacks, a series of images that share a single window, and it is multithreaded, so time-consuming operations can be performed in parallel on multi-CPU hardware. ImageJ can calculate area and pixel value statistics of user-defined selections and intensity-threshold objects. It can measure distances and angles[6].

1.4 Problem of the Study:

In any images processing techniques, there are many factors that lead to and interference with the signal or an image. CT images carry important information that needed for diagnosis. And the filters have been designed to remove noise and keep the important information of the image unaffected which this lead to reduce the patient doses to the lower level. And here combination between the CT software systems with image processing can lead to reduce the patient dose to the minimum. 1.5 Motivation Medical images carry very important information about our body organs and structures. The acquired tools used to collect the medical images usually produce some depending on the imaging modality. Image enhancement and denoising is needed for the diagnosis of patients. Designing a filter or building up an algorithm that will remove the noise and enhance the contrast of the medical image is very important. Image denoising with linear and non-linear filters become an interesting area of research in the recent years since it has the property of multi­ resolution. Furthermore, some filters make it very sufficient to denoising the images with less degradation compared to other digital filters. Therefore, to enhance a medical image efficiently, more images should be obtained with high frequency information. As a result, it is proposed for denoising medical images that will result in high quality. Some filter results in blurring effect or in reducing

- 4 - the data of the images while these data are one of the interests. Therefore, image quality assessment is needed to evaluate the filters. The goal of image quality assessment is to measure the strength of the perceptual similarity between denoised images and reference images. 1.6 Objectives of the Research

1.6.1 General objectives The main objective of this study is assessment of image quality during CT examinations using image processing.

1.6.2 Specific Objectives: .To denoise medical images using linear filter and non-linear filters ٠ To evaluate the performance of different filtering in removing noise from ٠ CT image. To measuring the Mean Square Error (MSE), Peak-Signal-to-Noise Ratio ٠ (PSNR) and the Structural Similarity Index Metric (SSIM). .To compare the performance of the different filters for denoising CT images ٠ 1.7 Overview of study: This study is concerned with use different type of filters to remove noise from CT images, it falls into five chapters. Chapter one will deal with introduction, and problem of the study, objectives and methodology. Chapter two will consists the literature review related to the current study. Chapter three will shows the methodology upon which the thesis carried out and chapter four will shows the results and discussion and chapter five will shows the conclusion, recommendation, some limitations faced the researcher and references.

- 5 - CHAPTER TWO LITERATURE REVIEW

2.1 Image processing

Image processing is processing of images using mathematical operations by using any form of signal processing for which the input is an image, a series of images, or a video, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two­ dimensional signal and applying standard signal-processing techniques to it. Images are also processed as three-dimensional signals where the third-dimension being time or the z-axis [4].

Image processing usually refers to digital image processing, but optical and analog image processing also are possible. This article is about general techniques that apply to all of them. The acquisition of images (producing the input image in the first place) is referred to as imaging [5]. Image processing contain three type of computerized processes low-, med-, and high-level process. Low -level process involve primitive operation such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its inputs and outputs are images. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. A mid-level process is characterized by the fact that its inputs generally ate images, but its outputs attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Finally, higher-level processing involves ''making sense‘‘of an ensemble of recognized

- 6 - objects, as in image analysis, and at the far end of the continuum. Performing the cognitive functions normally associated with vision [2]. Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area recognition of individual regions or objects in an image. Thus we call digital image processing encompasses process whose inputs and outputs are images and, in addition, encompasses process that extract attributes from images, up to and including the recognition of individual objects. As a simple illustration to clarify those acquiring an image of the area containing the text, preprocessing that image, extracting (segmenting) the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing [2]. 2.2 Medical Image Processing The image processing is now a critical component in science and technology. The influence and effect of digital images on current society is astonishing. Progress in computerized medical image reconstruction and related developments has propelled medical imaging into one of the most important sub-fields of scientific imaging, especially in analysis methods and computer-aided diagnosis. In medical image processing, the area of interest is being raised. It comprises an extensive range of methods and techniques, initiating with the acquisition of images by exploiting specialized devices, image enhancement and analysis, to 3D model reconstruction from 2D images captured by the device [7].The captured or digitized image will undergo the process of segmentation and extraction of important information before further processed in medical imaging for diagnosis. Medical images are usually of low contrast and due to various acquisitions, as well as display devices because of application of diverse types of quantization or reconstruction and enhancement algorithms. Image processing techniques have made it feasible to extract meaningful information from medical images with less

- 7 - degradation. The main objective of medical imaging is to acquire a high resolution image with as much detail as possible for the sake of diagnosis, analysis of disease, and other applications used for. To achieve the finest possible diagnoses, it is necessary that medical images be clear and free of noise and artifacts. Noise removal is one of the major challenges in the study of medical imaging. Consequently, they could mask and blur important but delicate features in the images, which reflect that the noise in medical images creates a problem. Therefore, medical image processing and analyzing focuses on the structural and functional [8]. 2.3 Medical Image Medical information composing of clinical data, images, and other physiological signals, has become an essential part of a patient's care, whether during the screening, diagnostic stage or treatment phase. Over the past three decades, rapid developments in Information Technology (IT) and medical instrumentation have facilitated the development of digital medical imaging. This development has mainly concerned Computed Tomography (CT), Magnetic Resonance Imaging (MRI), different digital radiological processes of vascular, cardiovascular and contrast imaging, mammography, diagnostic ultrasound imaging, nuclear medical imaging with Single Photon Emission Computed Tomography (SPECT), and Positron Emission Tomography (PET) (Prudhvi & Venkateswarlu, 2012). All these processes have produced ever-increasing quantities of images. These images are different from typical photographic images, primarily because they reveal internal anatomy as opposed to an image of the surface. Specificities of medical images in natural monochromatic or color images, the pixel intensity of the image corresponds to the reflection coefficient of natural light [1]. 2.4 Computed Tomography The term tomography refers to the general class of devices and procedures for producing two-dimensional (2D) cross-sectional images of a three­

- 8 - dimensional(3D) object. Tomographic systems make it possible to image the internal structure of objects in a noninvasive and nondestructive manner. By far the best known application is the computer assisted tomography (CAT or simply CT) scanner for X-ray imaging of the human body. In conventional X-ray radiography, a stationary source and a planar detector are used to produce a 2D projection image of the patient. The image has intensity proportional to the amount by which the X-rays are attenuated as they pass through the body, i.e., the 3D spatial distribution of X-ray attenuation coefficients is projected into a 2D image. The resulting image provides important diagnostic information due to differences in the attenuation coefficients of bone, muscle, fat, and other tissues in the 40-120 keV range used in clinical radiography [9]. X-rays passing through an object experience exponential attenuation proportional to the linear attenuation coefficient of the object. The intensity of a collimated beam of monoenergetic X-radiation exiting a uniform block of material with linear attenuation coefficient and depth d is given by

(2.1)

Where is the intensity of the incident beam. For objects with spatially variant attenuation along the path length z, this relationship generalizes to,

(z)dz (2.2(ع ت-ح0ل = I

Where) 1 (z)dzis a line integralthroughi (z). Letx, y, z) represent the 3D distribution of attenuation coefficients within the human body. Consider a simplified model of a radiography system that produces a broad parallel beam of

- 9 - X-rays passing through the patient in the z direction. An ideal 2D detector array or film in the (x, y)-plane would produce an image with intensity proportional to the negative logarithm of the attenuated X-ray beam, i.e.—Iogj-. The following

projection image would then be formed at the ideal detector:

(x,y) = b (x,y,z) dz ( 2'3) ٢

The utility of conventional radiography is limited due to the projection of 3D anatomy into a 2D image, causing certain structures to be obscured. For example, lung tumors, which have a higher density than the surrounding normal tissue, may be obscured by a more dense rib that projects into the same area in the radiograph. CT systems overcome this problem by reconstructing 2D cross sections of the 3D attenuation coefficient distribution. The concept of the line integral is common to both the radiographic projection (2.3)and CT. Consider the first clinical X-ray CT systems for which the inventor,G.Hounsfield, received the 1979 Nobel prize in medicine (the prize was shared with the mathematician A. Cormack)[10]. A collimated X-ray source and detector are translated on either side of the patient so that a single plane is illuminated, as illustrated in FIG.2.1 (i). After applying a logarithmic transformation, the detected X-ray measurements are a set of line integrals representing a 1D parallel projection of the 2D X-ray attenuation coefficient distribution in the illuminated plane. By rotating the source and detector around the patient other 1D projection can be measured in the same plane.

- 10 - FIGURE 2.1: (i) Schematic representation of a first-generation CT scanner that uses translation and rotation of the source and a single detector to collect a complete set of 1D parallel projections; (ii) The current generation of CT scanners use a fan X-ray beam and an array of detectors, which requires rotation only.

The image can then be reconstructed from these parallel-beam projections. One major limitation of the first generation of CT systems was that the translation and rotation of the detectors was slow and a single scan would take several minutes. X- ray projection data can be collected far more quickly using the fan-beam X-ray source geometry employed in the current generation of CT scanners as illustrated in Fig. 2.1(ii). Since an array of detectors is used, the system can simultaneously collect data for all projection paths that pass through the current location of the X- ray source. In this case, the X-ray source need not be translated, and a complete set of data is obtained through a single rotation of the source around the patient. Using this configuration, modern scanners can scan a single plane in less than one second. Recently developed spiral CT systems allow continuous acquisition of data as the patient bed is moved through the scanner. The detector traces out a helical orbit with respect to the patient allowing rapid collection of projections over a 3D volume. These data require special reconstruction algorithms. In an effort to

- 11 - simultaneously collect fully 3D CT data, a number of systems have been developed that use a cone-beam of X-rays and a 2D rather than 1D array of detectors [11]. Cone-beam systems are now widespread in clinical CT and they also play an important role in industrial applications. In X-ray CT data the high photon flux produces relatively high signal-to-noise ratio. However, the data are corrupted by the detection of scattered X-rays that do not conform to the line integral model. Calibration procedures are required to compensate for this effect as well as for the effects of variable detector sensitivity. A final important factor in the acquisition of CT data is the issue of sampling. Each 1D projection is undersampled by approximately a factor of two in terms of the attainable resolution as determined by detector size. 2.5 The quality of a digital image Any imaging system must be judged on the quality of the images it produces. For medical imaging systems the images must be diagnostically useful, that is capable of leading to the detection and identification of an abnormality and its interpretation so as to determine its cause, and obtained at an acceptable dose to the patient. An image is a spatial pattern of intensities. Fundamentally, the quality of a digital image depends on the size of the pixels, relative to the size of the image, and the number of available values of gray tone that are accessible to describe the intensity range between black and white: image quality is highest for small pixels and a large number of available gray tones[3]. 2.5.1Spatial resolution and pixel size Spatial resolution is a measure of the ability of the image to show fine detail. It can be reported as the minimum separation of small features in the object that can just barely be distinguished as separate in the image. The spatial resolution of a digital image depends on the resolution of the imaging system that produced it, characterized by its point spread function, PSF, or equivalently by its modulation transfer function, MTF, and the size of the pixels used to represent the digitized

- 12 - image. This latter is determined, either by the sampling frequency used in digitization or by the size and separation of the detectors in a two-dimensional detector array. Shows an image and the effect of increasing pixel sizes after acquisition. The larger the pixel sizes, the less detail. In fact the pixel size determines the smallest detail, increasing the pixel size reduces the spatial resolution, since it reduces the ability to see fine detail within the image. The point spread function of an imaging system is the image that results from a point object, and thus its width determines the size of the smallest observable detail in an analog image. Figure2.2 shows the point spread function of a two­ dimensional digital imaging system, with different-sized pixel grids superimposed on it. The smaller the pixels of the grid the more detail can potentially be displayed. Taking into account the sampling theorem we might choose the pixels to be about half the width of the point spread function to adequately sample it (Fig. 2.18 (a)). There is nothing to be gained from using much smaller pixels than this, since there is no additional detail within the point spread function (Fig. 2.18 (b)); at best it may produce a cosmetically more appealing image with less evident pixelation, but at the cost of a larger file size. On the other hand, using a larger pixel size would be detrimental to image quality, since the point spread function would spread to occupy a single large pixel (Fig. 2.2 (c))[3].

- 13 - Figure 2.2: Grids of various sizes superimposed on the point spread function image. (a) The optimal pixel size. (b) Smaller pixels offer little advantage since there is no detail within the point spread function. (c) Larger pixels are not recommended since the analog point spread function would spread (not shown) to occupy a single pixel.

There is an argument for reducing the pixel size a little further than one-half of the width of the point spread function, since the point spread function of the underlying, analog, imaging system does not have a brick-wall shape. If the point spread function is taken as Gaussian in shape, which is appropriate for most systems, and its width is taken as the full width at half maximum height (FWHM) of its point spread function, then the sampling distance, i.e. pixel size, should be about one-third of the FWHM to avoid significant loss of spatial resolution, thus:

2.4)

This represents a somewhat tighter restriction on pixel size, and is widely used as the rule-of-thumb in nuclear medicine imaging [3]. 2.5.2Brightness resolution Different features in an image are displayed as different shades of gray. In medical images these differences are determined in part by the properties of the tissues, such as their thickness, density and chemical composition, and by aspects of the

- 14 - imaging process that are controllable, such as the energy of the x-ray photons. It is crucial to be able to distinguish parts of the body that differ anatomically or physiologically only a little from each other. This requires a sufficient number of different gray values so parts which we want to be able to distinguish are assigned different gray values [3]. The number of gray values available in an image depends on the number of bits used during quantization: using n bits per pixel results in 2n shades of gray. Digitization with an 8-bit analog-to-digital converter, resulting in 256 possible shades of gray, is more than adequate for most visual purposes, since our visual system can distinguish only about 30 shades of gray, and most computer monitors have been manufactured to reproduce 256 gray levels. Images displayed with an insufficient number of shades of gray, i.e. fewer than about 32, suffer from false contouring or posterization, an effect mimicking topographic contours, which is most noticeable in areas of constant gray level. Some images require higher brightness resolution if quantitative analysis of image properties are to be made accurately. For example, the pixels in x-ray CT images have12-bit depth and therefore can have 4096 ( ) possible values. 2.5.3Noise content Noise is the unwanted, random (stochastic) fluctuations in an image. The principal sources of noise in a digital imaging system are photon (or quantum) noise, which arises from the discrete nature of electromagnetic radiation and its interactions with matter, and electronic noise in detectors or amplifiers. If a film-screen cassette is used to acquire the image, individual grains within the film and fluorescent screen produce random variations in the film density: the noise contribution from the screen (structure mottle) is larger than that from the film (film granularity). The process of digitization is also responsible for adding noise (quantization noise) to an image. Photon (or quantum) noise usually obeys the Poisson distribution function, and electronic noise is almost always Gaussian [3].

- 15 - These unwanted stochastic variations can be quantified most easily in a region of the image that is expected to have a constant brightness. The noise power (PN) can be taken as the variance, i.e. the square of the standard deviation, of the pixel values in such a region. To understand its significance, the noise should be compared to the average power or intensity of the signal (PS), which is given by the average value of the pixels in the image. The signal-to-noise ratio (SNR or S/N) is the ratio of the intensity of the signal to the noise power; it is often expressed in (dB) by taking ten times the logarithm, to the base 10, of the ratio. Thus:

(خ (SNR (dB) = 101 og JO (2٠5

The noise produced within an imaging system is a combination of several noise sources, and it may not be possible to identify them separately. The image produced by a uniform gray scene, which should result in a uniform brightness, has a distribution of gray tones around an average value; the width of the distribution is a measure of the noise content of the image. In medical x-ray and Y-ray imaging systems, the number of photons emitted per unit time from the source varies and so too do its interactions with the patient's body. The result of both these factors is that the image has a spatial and temporal randomness. This source of noise, often referred to as quantum noise, is a fundamental and unavoidable noise source in medical imaging. In a good medical imaging system, quantum noise, which is unavoidable, is the dominant source of random fluctuation. Quantum noise is characterized by Poisson statistics, which is used to describe independent counting events, especially when the events are comparatively infrequent. An important characteristic of the Poisson distribution is that the standard deviation in the number of counts is numerically equal to the square root of the mean of the counts

- 16 - where N is the number of photons carrying the signal). The relative ٠ = SD) width of the distribution decreases as the mean grows larger; thus,

(٧١=١ المنب/Relative variation 2.6) 1 Since N is a measure of the signal strength. The signal-to-noise ratio increases as the mean gets larger: thus, the greater the number of x-ray or Y-ray photons that can be detected, the higher the image signal-to-noise ratio and the less noisy it appears [3]. 2.5.3.1 Type of noise There are many types of noises occurs in medical images during acquisition and transmission. Mostly occurred noise are Gaussian noise, Speckle noise, salt and pepper noise, Rician noise, and so on. The Rician noise corrupts the MRI images while the Speckle noise corrupts the ultrasound images. When an electron is generated thermally at the sensor site, dark current noise is introduced in an image. Therefore, its sensor is temperature dependent [12]. This present the essential description of the most commonly present types of noise that corrupts the images during its acquisition or transmission process. (i) The The white or Gaussian noise is the most common type of noise where its power is uniformly dispersed over the spectral and spatial spaces and its mean is zero. This type of noise has a probability density function (PDF) of the normal distribution (also known as Gaussian distribution). It most commonly presents as additive noise to be called additive white Gaussian noise (AWGN) as described by previous researchers. Gaussian noise generates a series of noise having a Gaussian normal distribution function according to the probability density function equation [9].

- 17 - (i) (ii) (iii)

(iv) Figure 2.3: Noise and its histogram(i) test pattern image for demonstrating the addition of noise, (ii) histogram of image in (i), (iii) addition of Gaussian noise to image in (i),(iv) histogram of image in (iii)[2].

(ii) Quantization noise Quantization noise is inherent in the amplitude quantization process and occurs in the analog-to-digital converter, ADC, when sampled values are fitted to a finite number of levels. The noise is additive and independent of the signal when the number of bits n > 4. For a digitized signal that is bounded with a minimum and maximum pixel value, and , respectively, the signal-to-noise ratio, SNR, is given by

(2.7)

- 18 - Where is the standard deviation of the noise. When the input signal is a full- amplitude sine wave it can be shown that the SNR becomes (2.8)

Quantization noise can usually be ignored as the total signal-to-noise ratio of a complete system is typically dominated by the smallest signal-to-noise ratio of a component of the system, i.e. the largest noise. In semiconductor detectors this is photon noise [3]. (iii) Colored noise , 1/f noise or has a power that is proportional to the reciprocal of the frequency. Over an octave, a doubling of frequency, it drops to half power, i.e. it drops off at 3 dB per octave. Brown noise, Brownian noise or red noise has a power spectral density that is proportional to the reciprocal of the square of the frequency. Over an octave it drops to one-quarter of its power, i.e. it drops off at 6 dB per octave. Graphically, Brown noise mimics , the random movement of particles suspended in a fluid. Brown noise can be produced by integrating white noise [3]. (iv) Poison Noise The nonlinear response of the image detectors and recorders generate poison noise. This noise is image data dependent. This expression arises because detection and recording processes include random electron emission having a Poisson distribution with a mean response value. Since the mean and variance of a Poisson distribution are equal, the image dependent term has a standard deviation if it is assumed that the noise has a unity variance [12].

- 19 - FIGURE 2.4: Sanfrancisco image corrupted by Poisson noise [13]. (v) Impulse (or salt-and-pepper) noise. Another common form of noise is data drop-out noise, commonly referred to as impulse noise or salt-and-pepper noise. Here, the noise is caused by errors in data transmission. Corrupted pixels are either set to the maximum value or to zero, giving the image a “salt and pepper” like appearance. Unaffected pixels remain unchanged. The noise is usually quantified by the percentage of pixels which are corrupted [3].

(i) (ii) Figure 2.5: Noise (i) image in figure 2.5 (i) corrupted by salt-and pepper noise and (ii) its histogram [2].

- 20 - (vi)Periodic noise This arises typically from electrical interference, especially in the presence of a strong mains power signal during image acquisition. It is spatially dependent and generally sinusoidal at multiples of a specific frequency. It is recognizable as pairs of conjugate spots in the frequency domain, and can be conveniently removed either manually or by using a notch (narrow band reject) filter [3]. 2.6 Image Denoising Techniques Image denoising is done by filtering or designing tools to remove the noise. Filtering divide in broad categories depends on its operation or efficiency. Denoising of images in medical science is still a challenging problem up to today. There are so many techniques and algorithms published, where each has their own assumptions limitations and advantages. Common methods of image denoising are spatial domain and transform domain. The linear filter such as Weiner and non­ linear threshold filtering, wavelet coefficient model, non-orthogonal wavelet transform, wavelet shrinkage, anisotropic filtering and trilateral filtering are used in denoising. Examples of spatial filtering are mean filtering and Gaussian filtering. The results obtained by linear filters are not good because they destroy the fine details and lines and blur the sharp edges of the images. is used recently to denoise the images since it work effectively with high frequency areas, but it fails to work at low frequency. In fact, it fails to remove salt and pepper noise and gives low performance to remove speckle noise. It can be concluded that each technique or filter or algorithm has its own advantages and limitations or drawbacks. Up to now, there are so many filters for medical image denoising but medical image denoising is still a challenging problem [14]. 2.6.1Linear filters Linear filtering is filtering in which the value of an output pixel is a linear combination of the values of the pixels in the input pixel's neighborhood. In linear filters the convolution process is used for implementing the neighboring kernels as

- 21 - neighborhood function. The following section describes the linear filters such as mean filter and Gaussian filters. (i) Mean filter The idea of mean filtering is simply to replace each pixel value in an image with the average value of its neighbors, including itself. This has the effect of eliminating pixel values which are unrepresentative of their surroundings. Mean filtering is usually thought of as a convolution filter. Like other convolutions it is based around a kernel, which represents the shape and size of the neighborhood to be sampled when calculating the mean [2] [15].

Figure 2.6: Denoising with mean filter(i) Original image, (ii)mean filter applied with mask ,(iii)mean filter applied with mask ,(iv)mean filter applied with mask ,(v)mean filter applied with mask ,(vi)mean filter applied with mask [2].

- 22 - (a)Arithmetic mean filter: This is simplest of the mean filter, let represent the set of coordinates in a rectangular subimage window of size , centered at point (x, y). The arithmetic mean filtering process computes the average value of the corrupted image g(x, y) in the area defined by [2]. The value of the restored image at any point (x, y) is simply the arithmetic mean computed using the pixels in the region defined by . In other words,

(2٠9) ٢٦ 1 ^'٥)I g ث=0د'يبر

(s,t')ESxy

This operation can be implemented using a convolution mask in which all coefficients have value —-—. A mean filter simply smoothes local variation in an ٠ ٠ ٠ py ٠ mxn ٠ image. Noise is reduced as a result of blurring. (b)Geometric mean filter: An image restored using a geometric mean filter is given by the expression

(2.10) f(x,y) = [n(s,t)es ]mn

Here, each restored pixel is given by the product of the pixels in the subimage window, raised to the power^. A geometric mean filter achieves

comparable to the arithmetic mean filter, but it tends to lose less image detail in the process [2]. (c)Harmonic mean filter: The harmonic mean filtering operation is given by the expression

- 23 - mn (2.11) f(x,y)= 1 (SXy g (s t ج (s t) ة

The harmonic mean filter works well for salt noise but fails for pepper noise. It does well also with other type of noise like gaussian noise [2]. (d)Contraharmonic mean filter: The contraharmonic mean filtering operation yields a restored image based on the expression

(2.12) ة ة = (f(x,y

Where Q is called the order of the filter. This filter is well suited for reducing or virtually eliminating the effects of salt-and -pepper noise. For positive value of Q, the filter eliminates pepper noise. For negative value of Q it eliminates salt noise. It cannot do both simultaneously. Not that the contraharmonic mean filter reduces to the arithmetic mean filter if Q = 0, and to the harmonic mean filter if Q = -1 [2]. (ii) Wiener Filter The Wiener filtering executes an optimal tradeoff between inverse filtering and noise smoothing. It removes the additive noise and inverts the blurring simultaneously. Wiener filter estimates the local mean and variance around each pixel [2][15][16].

(ni,n2) (2.13)ملج2أ٠7لأ7ةغ = عم And

(ni, n2) —M 2 (2.14) تجج2إ¥ةث=٢2ح

- 24 - Where is the N-by-M local neighborhood of each pixel in the image, then creates a pixel-wise wiener filterusing these estimates,

(a(ni,n2) - g (2.15)آ+غم=(n2,لb0

Where the noise variance is not given, then the average of all the local estimated variances. Wiener is a frequency domain filter, which removes Gaussian noises from the images. (iii) Gaussian filters Are the only ones which are separable and, at least to a lattice approximation, circularly symmetric. They also overcome the other stated drawback of moving average filters because weights decay to zero. Gaussian filters have weights specified by the probability density function of a bivariate Gaussian, or Normal, distribution with variance , that is

(2.16) [-٠ر,٤ = - [3 ٠٠], ■ ■ ■ --,[3 or٠/{ ر ئءل exp مد = ٧ لة

For some specified positive value for٠2. Here 'exp' denotes the exponential ٠are chosen Limits of± 3 .٠ represents the 'integer part' of3 [٠ function and [3 because Gaussian weights are negligibly small beyond them. Note, that the divisor of ensures that the weights sum to unity (approximately), which is a common convention with smoothing filters. If = 1, the array of weights is

- 25 - /0 0 12 1 00 ١ 30 13 1322 3 0/ 1 13 5997 59 131 (2.17) 222 97 97159 22 2 ت 0، 1000 1 13 59 97 59 131 \0 3 13 22 13 30 (0 10 12 0 ١0

(For succinctness of notation, we show a 7 7 array to represent the weights for k; l = -3, . . ., 3, and we have specified the weights only to three decimal places— | hence the divisor of 1000 at the beginning of the array.)[l7].

(i) (ii) (iii) Figure 2.7: Denoising by Gaussian filter, (i) Original “Mercy”; (ii) Image corrupted by salt and pepper noise; (iii) Denoised by blurring with a Gaussian filter [13].

2.6.2Non - linear filters Linear filters may lead to the blurring of edges. To overcome such a problem non­ linear filters are used for . These filters help to preserve edges. The following section describes the non-linear filters such as median filter and entropy based filters.

- 26 - (i) Median Filter: Median filter is an example of non-linear filters. In median filter, the ranking of the neighboring pixels is done according to the intensity or brightness level and value of the pixel under evaluation is replaced by the median value of surrounding pixel values[2][15]. (2.18)

The original value of the pixel is included in the computation of the median. An example of median filtering of asingle 3x3 window of values is shown below.

Center value (previously 2) is replaced by the median of all nine values (4). Median filters are quite popular because, for certain types of random noise, they provide excellent noise-reduction capabilities, with considerably less blurring than linear smoothing filters of similar size. Median filters are particularly effective in the presence of both bipolar and unipolar impulse noise.

- 27 - Figure 2.8:(i) X-ray image of circuit board corrupted by salt-and pepper noise. (ii) Noise reduction with a mean filter. (iii) Noise reduction with a median filter. (Original image courtesy of Mr. Joseph E.Pascente, Lixi.Inc).

(ii) Entropy Filter: Entropy filters replacing every value by the information entropy of the values in its range of the specified neighborhood. Entropy Filter computes the information entropy of the values in (2r+1) X (2r+1) blocks centered on each pixel. Given a set of values the information entropy is calculated by

-ZPjlO g(P);(2.19) Entropy filtering can reveal JPEG compression artifacts and reveals the presence of padding in an image.

(iii) Max filter This filter is useful for finding the brightest point in an image. Also, because pepper noise has very low values. It is reduced by this filter as a result of the max selection process in the subimage area [2]. (2.20)

- 28 - (iv) Minimum filter This filter is useful for finding the darkest in an image.Also it reduces salt noise as a result of the min operation[2]:

(2.21)

2.7Image Quality Assessment Methods Measurement of image quality is crucial in many image processing systems. Due to inherent physical limitations and economic reasons, the quality of images, audios, and videos could visibly degrade right from the point when they are captured to the point when they are viewed by a human observer. Finding the image quality measures that have high sensitivity to these would help systematic design of coding communication and imaging systems and improving or optimizing the image quality for a desired quality of service at a minimum cost. Measurement of image quality is very important to numerous image processing applications. Human being is highly visual creatures, which mean it can recognize each other from appearance. The main function of the human eye is to extract structural information from the viewing field, and the human visual system (HVS) is highly adapted for this purpose. Therefore, for the applications, in which images are ultimately to be viewed by human beings, the only accurate method of quantifying visual image quality is through subjective evaluation or assessment. However, subjective evaluation is usually too inconvenient because it is time­ consuming and expensive. As a result, a lot of efforts have been made to develop objective image quality metrics that correlate with perceived quality [4]. The goal of image quality assessment research is to design a method to measure the strength of the perceptual similarity between the test and the reference images. Many approaches have been taken in account by researchers to measure the quality of an image or for the assessment of images.

- 29 - The first approach is called the error sensitivity approach. The test images data is considered as the sum of the reference image and an error signal, where it is assumed that the loss of perceptual quality is directly related to the visibility of the error signal. Most of HVS-based image quality assessment models attempt to weigh and combine different aspect of the error signal according to their respective visual sensitivities, which are usually determined by psychophysical measurement. The short coming from this approach is that larger visible differences may not necessarily imply lower perceptual quality. In the second approach, the observation process efficiently extracts and makes the use of information represented in the natural scene, whose statistical properties are believed to play a fundamental role in the development of the HVS. A clear example of the second approach is the structural similarity based image quality assessment method. This method is based on observation that natural images are highly structured, meaning that the signal samples have strong dependencies among themselves. These dependencies carry important information about the structure of the objects in the visual scene [18]. The most commonly used objective image quality measures are; Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM). 2.7.1Signal-to-Noise Ratio (SNR) Signal-to-noise ratio is also called as SNR or S/N; defined as the ratio of signal power to the noise power corrupting the signal. The Signal-to-Noise Ratio (SNR) is a defining factor when it comes to quality of measurement. A high SNR guarantees clear acquisitions with low distortions and artifacts caused by noise. The better your SNR, the better the signal stands out, the better the quality of your signals, and the better your ability to get the results you desire. SNR measurement is commonly used in the field of science and engineering fields. A ratio higher than 1:1 indicates more signal than noise. While SNR is commonly quoted for electrical

- 30 - signals, it can be applied to any form of signal (Naomi, 2009).The SNR is given by the following formula:

varQavgOdenoised((): 22( . )م٠،/(لج( 1B I>٢SNR١

2.7.2Mean Square Error (MSE) This metric is frequently used in signal processing. The goal of a signal fidelity measure is to compare two signals by providing a quantitative score that describes the degree of similarity and the level of error or distortion between them. Typically, it is assumed that one of the signals is a pristine original, while the other is distorted or contaminated by errors as some noise [19]. The MSE is defined as follows:

(٠ن)} no،(،,)) - /"enoised(2.23) 2،ri٥أرة 1 {/٠!Ms E = — EjL Where N, M: give the size of the image. , : is the pixel values at location (i, j) of the original image before the

denoising process. , : is the pixel values at location (i, j) of the image after denoising algorithm. 2.7.3Peak Signal to Noise Ratio (PSNR) PSNR stands for the peak signal-to-noise ratio. It is an engineering term used to calculate the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Because many signals have a very wide , PSNR is usually expressed in terms of the logarithmic decibel scale [20]. The PSNR computes as: (ق وPSNR = 2 0 ،02.24

Where MSE denotes the mean square error estimated between the original image and the denoised image.

- 31 - 2.7.4Structural Similarity Index Metric (SSIM) The structural similarity index (SSIM) method is a recently proposed approach to image quality assessment. It is widely believed that the statistical properties of the natural visual environment play a fundamental role in the evolution, development, and adaptation of the human visual system (HVS). An important observation about natural image signal samples is that they are highly structured. Structuring signal means that the signal sample exhibit strong dependencies amongst themselves, especially when they are spatially proximate. These dependencies carry important information about the structure of the objects in the visual scene. The principal hypothesis of structural similarity based image quality assessment is that the human visual system is highly adapted to extract structured information in the visual field, and therefore, a measurement of structure similarity or distortion should provide a good approximation to perceived image quality. The SSIM index is a method to measure the similarity or the differences between two sets of images. SSIM index is a full reference metric, in other words, the measure event of image quality is based on an initial uncompressed or distortion free image as reference [21]. The SSIM is computes as:

)25ججج=ك*(د

Where % is the average of x.

)1 )2.26 x٠ =مة X

X: is the average of y.

)y 1 )2.27 ئ،ةتي = ٧

)x i — x( 1(2)2.28 ع؛ةخ = ٠x.a2 is variance of x

)yr y( 1( 2)2.29 ئإذغ = ٠y.٠yis variance of y

- 32 - : represents the standard deviation of x and y.

Xi-x)(yi-y) (2.0) ع؛ةث=٢ل٠ 1

; Two variables to stabilize the division with weak denominator, L represents the dynamic range of the pixel values, and = 0.01 and = 0.03 by default.

- 33 - 2.8Previous Related Works S. SIVAKUMAR1 &etal (2014) proposed a comparative study on image filters for noise reduction in lung CT scan images and obtained results for filters namely mean filter, wiener filter, median filter and entropy filter. By investigating the comparison parameters, they found it is clear that wiener filter performs very well compare with the other filtering methods. The wiener filter provides the maximum PSNR, Image quality index value and minimum MSE, RMSE, and MAE. The wiener filter reduces noises in lung CT scan images for both the cancer and non­ cancer images. Arin H. Hamad&etal (2014) proposed de-noising of medical images by using some filters. Removal of noise from the medical images was done using different filters; selected images were split into two different formats: JPG and TIF. Some images have noise by different types of noise. Different noises have been considered, and they are: Poisson, speckle and Gaussian. De-noising techniques (using MATLAB programming) were used to restore the mentioned noises on the images. Different types of filters were used to remove the noises such as Average filter, Gaussian filter, Log filter, Median filter, and Wiener filter. At the final stage, the results of the mentioned filters were compared to get the best and suitable filter for the images of the cell and breast. Image quality parameters: MSE, SNR, and PSNR were considered as the main parameters for the comparison. The results verified that the Gaussian filter is a suitable filter to remove the noise in the medical images. S.Senthilraja&etal (2014) proposed noise reduction in computed tomography image using WB-filter. In Medical Imaging, Noise degrades the quality of images. This degradation includes suppression of edges, blurring boundaries etc. Edge and preservation details are very important to discover a disease. Noise removal is a very challenging issue in the Medical Image Processing. Denoising can help the physicians to diagnose the diseases. Medical Images include CT, MRI scan, X-ray

- 34 - and ultrasound images etc. This paper they implemented a new filter called WB- Filter for Medical Image denoising. WB-Filter mainly focuses on speckle noise & Gaussian Noise removal especially in the CT scan images. Experimental results are compared with other three filtering concepts. The result images quality is measured by the PSNR, RMSE and MSE. The results demonstrate that the proposed WB - Filter concept obtaining the optimum result quality of the Medical Image. BhausahebShinde&etal (2012) proposed study of noise detection and noise removal techniques in medical images. They took different medical images like MRI, Cancer, X-ray, and Brain and calculated standard deviations and mean of all these medical images. To finding salt & pepper noise and then applied median filtering technique for removal of noise. After removing a noise by using median filtering techniques, Adaptive Filtering and Average Filtering. The results are analyzed and compared with standard pattern of noises and also evaluated through the quality metrics like Mean, and Standard deviation. Through this work they have observed that the choice of filters for de-noising the medical images depends on the type of noise and type of filtering technique, which are used. It is remarkable that this saves the processing time. GnanambalIlangoand B. Shanthi Gowri (2012) presented study of neighborhood median filters to remove speckle noise from CT - images.Removal of noise from the medical images is very challenging in image processing. In recent years, technological development has improved significantly in analyzing medical imaging. This paper proposed different filtering techniques for the removal of speckle noise from CT medical images by topological approach. The filters are constructed based on metric topological neighborhood. The quality of the enhanced images is measured by the statistical quality measures: Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR). H. Oulhaj&etal (2012) proposed noise reduction in medical images -comparison of noise removal algorithms. The Medical community uses several image

- 35 - acquisition techniques for diagnosing and suggesting the corresponding therapies. Therefore the obtained images from clinical examinations should be treated to assist doctors in results interpretation. In this paper, they focus on denoising task in order to determine the benefits and drawbacks of each denoising algorithm. For this, they used as data set the most common acquisition tools namely: Magnetic Resonance (MR), Computed Tomography (CT), Ultrasounds, Scintigraphy and X- Ray images. For measuring the denoised image quality we used : Signal to Noise Ratio (SNR), Peak to Signal noise (PSNR) Root Mean square Error (RMSE) and the Mean Structure Similarity Index (MSSIM).

- 36 - CHAPTER THREE Methodology 3.1 Material Images ware acquisitioned using Toshiba CT scanner 64 slices in Royal Care International Hospital. CT scanner that participated in this study was helical CT scanner. The images were collected as DICOM format from the radiological department of Royal Care International Hospital.

Fig 3.1:Show Computed Tomography (CT), 64 slice(Toshiba.

Fig 3.2: Show Control of Computed Tomography (CT).

- 37 - 3.2 Place and time of study:

This study was performed at Radiology department of Royal Care International

Hospital during the period from (February2017 up to July -2017).

3.3Study sample:

This study included 100 abdominal CT images and 100 brain CT images, all selected from adult patients who were referred to the CT Scan (Brain and abdominal CT) in radiological department at Royal Care International Hospital.

3.4 Study variables:

The variables that were collected from each subject included gender, adult and

images in DICOM Format.

3.5 Test images format

Images were collected in CDs in DICOM format from CT scanner and then converted to JEPG format using ImageJ software.

3.6Analysis of data: Images area (size) and pixels values mean, max, min and stander deviation were calculated by analyzed images in ImageJ and they use in calculation for the image quality parameters, then used as input to the Microsoft excel for analysis. 3.7 ImageJ software:

ImageJ is an open source image analysis program. The program provides a plugin framework for adding custom functionality. There are many analysis routines built in to ImageJ and hundreds more can be freely downloaded from the web.

- 38 - Figure 3.3 show ImageJ software window.

For analyzed image we used in the first File open, to opining image, then using Analyze Measure for calculating the area of image and pixel values. For applying filters we used process filters and then chosen the type of filter we needs.

Figure 3.4:Show the image in ImageJ software and analyses window.

For calculating MSE between two images (original and denoised image) first opening two images and then used process image calculator. For calculating SSIM between two images (original and denoised image) first opening two images and then used plugins SSIM index.

- 39 - CHAPTER FOUR RESULTS In our experiment, we use abdomen and brain CT scan from Royal Care International Hospital and each slice of images in the size of 512 X 512 in DICOM format. At the first stage, the following filters (Minimum, Gaussian, Median, and Mean) were used on the original image (abdomen) to remove noise. It was found that all the filters have different ability in removing noise, as shown in Figure4.1. On the other hand, in order to obtain the optimum filter on the original image, it was found that the Median filter has more ability and is more successful to remove noise; this depended on the image quality parameters of SSIM and PSNR in dB, because the value of PSNR and SSIM has a maximum value for median filter. The results are listed in Table 4.2, and are shown in Figures 4.1 to 4.4.

(a) (b) (c)

(d) (e) Figure 4.1 :Show different linear and Non-Linear Filters Applied for abdominal CT scan Image: (a) Original Image (b) Gaussian Filter Applied (c) Minimum Filter Applied (d) Median Filter Applied and (e) Mean Filter Applied Image.

- 40 - Image quality Measures MSE PSNR SSIM Linear filters Mean 13 37.02 0.96 Gaussian 18.39 35.51 0.92 Non-linear filters Median 6.717 39.89 0.97

Minimum 55.847 30.69 0.78

Table 4.1: Show Image Quality Measures for Both the Linear and Non-Linear Filters for Abdomen CT scan Image in figure 4.1.

Image quality Measures Average value of 100 image MSE PSNR SSIM Linear filters Mean 10.2 38.26 .96 Gaussian 14.46 36.66 .93 Non-linear filters Median 4.82 41.54 .98

Minimum 47.02 31.55 .81

Table 4.2: Image Quality Measures for Both the Linear and Non-Linear Filters average values of 100 Abdomen CT scan Images.

- 41 - Figure 4.2 Show the Histogram of MSE and several filters Applied for Abdomen CT images.

Figure 4.3: Show the Histogram of PSNR and several filters Applied for Abdomen CT images.

- 42 - Figure 4.4 Show the Histogram of SSIM and several filters Applied for Abdomen CT images. More so, it is observed that the filters (Gaussian, Minimum, Median, and Mean) were applied on the original image (brain) to remove noise. It was found that all the filters have different ability in removing noise, as shown in Figures 4.5. Finally, in order to obtain the optimum filter on the original image (brain), it was found that the Median filter has more ability and is more successful to remove noise, this depended on the image quality parameters of SSIM and PSNR in dB, because the value of PSNR and SSIM has a maximum value for median filter. The results are listed in Tables 4.4, and are shown in Figures 4.5 to 4.8.

- 43 - (a) (B) (C)

(D) (E) Figure 4.5: Different linear and Non-Linear Filters Applied for Brain CT scan Image: (a) Original Image (b) Gaussian Filter Applied (c) Minimum Filter Applied (d) Median Filter Applied and (e) Mean Filter Applied Image

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.98 5.837 40.5029 Gaussian 9.537 38.37068 0.96 Non-linear filters Median 0.99 1.798 45.6169 Minimum 0.89 30.924 33.26184

Table 4.3: Show Image Quality Measures for Both the Linear and Non-Linear Filters for Brain CT scan Image in figure 4.1.

- 44 - Image quality Measures Average value of 100 image

MSE PSNR SSIM

Linear filters Mean 8.17 39.41 .98

Gaussian 12.95 37.32 .95

Non -linear Median 3.05 44.52 .99 filters Minimum 38.53 32.78 .87

Table 4.4:ShowImage Quality Measures for Both the Linear and Non-Linear Filters average values of 100 Brain CT scan Images.

Figure 4.6:ShowHistogram of MSE and several filters Applied for brain CT images.

- 45 - 50

Figure 4.7:Show Histogram of PSNR and several filters Applied for brain CT images.

Figure 4.8:ShowHistogram of SSIM and several filters Applied for brain CT images.

- 46 - CHAPTER FIVE

5.1 Discussions: This study intended to assessment of image quality in CT images by reduction of noise in images using different type of filters to help in order to obtain the optimum filter reducing the noise from abdomen and brain CT images. The test images were collected from Royal Care International Hospital department of radiological. In Medicine, doctors are faced to the problem of recovering images from noisy and incomplete produced images. Hence a denoising process should remove artifacts and noise. Generally, it's difficult to suggest an efficient noise removing method while conserving anatomical details and for all medical images modalities. Appling filters (linear or non-linear) in abdomen CT images have different ability in removing noise as shown in figure 4.1(a) the original abdomen CT image, the image quality parameters (MSE, PSNR, SSIM) after applied Gaussian filter is (18.39, 35.51, 0.92) respectively as shown in figure 4.1(b), the (MSE, PSNR, SSIM) is (55.84, 30.69, 0.78) respectively after applied minimum filter as shown in figure 4.1(c), the (MSE, PSNR, SSIM) is (6.71, 93.89, 0.97) respectively after applied Median filter as shown in figure 4.1(d), and the (MSE, PSNR, SSIM) is (13, 37.02, 0.96) respectively after applied Mean filter as shown in figure 4.1(e).

Appling filters (linear or non-linear) in brain CT images have different ability in removing noise as shown in figure 4.5, (a) the original brain CT image, the image quality parameters (MSE,

- 47 - PSNR, SSIM) after applied Gaussian filter is (9.53, 38.37, 0.96) respectively as shown in figure 4.5(b), the (MSE, PSNR, SSIM) is (30.92, 33.26, 0.89) respectively after applied minimum filter as shown in figure 4.5(c), the (MSE, PSNR, SSIM) is (1.79, 45.61, 0.99) respectively after applied Median filter as shown in figure 4.5(d), and the (MSE, PSNR, SSIM) is (5.83, 40.50, 0.98) respectively after applied Mean filter as shown in figure 4.5(e). Image quality parameter (MSE, PSNR, SSIM) were collected by analysis of each image from specific hospital using ImageJ software. The mean of the MSE (10.2, 14.46, 4.82, 47.02) after applied (mean, Gaussian, median, minimum) filters for abdominal CT scan images respectively as shown in table4.2 and figure4.2. And the mean of the MSE (8.17, 12.95, 3.05, 38.53) after applied (mean, Gaussian, median, minimum) filters for brain CT scan images respectively as shown in table4.4 and figure4.6. The mean of the PSNR (38.26, 36.66, 41.54, 31.55) after applied (mean, Gaussian, median, minimum) filters for abdomen CT scan images respectively shown in table4.2 and figure4.3. And the mean of the PSNR (39.41, 37.32, 44.52, 32.78) after applied (mean, Gaussian, median, minimum) filters for brain CT scan images respectively as shown in table4.4 and figure4.7. The mean of the SSIM (.96, .93, .98, .81) after applied (mean, Gaussian, median, minimum) filters for abdomen CT scan images respectively shown in table4.2 and figure4.4. And the mean of the SSIM (.98, .95, .99, .87) after applied (mean, Gaussian, median, minimum) filters for brain CT scan images respectively as shown in table4.4 and figure4.8.

- 48 - The results obtained show that filters have different ability in removing noise from the both abdomen and brain CT scan images (different in image quality parameter after applied filters) and obtained that median filter has more ability and is more successful (maximum PSNR and SSIM) in removing noise from both abdominal and brain CT scan images as shown in (tables 4.2,4.4andfigure 4.1 to 4.8). In comparison with S. SIVAKUMAR and BhausahebShindetheir results come to in an agreement showing the median filter performs better when comparing with mean filter in removing noise from CT images. InArin H. Hamad study the results verified that the Gaussian filter is a suitable filter to remove the noise in the medical images and this result come in to disagreement with our results because in our study results median filter work better than other filters applied in removing noise from CT images.

- 49 - 5.2 CONCLUSIONS This study was intended to assessment of image quality in CT images by reduction of noise in images using different type of filters to help in order to obtain the optimum filter reducing the noise from abdomen and brain CT images. The images were collected from Royal Care International Hospital department of radiological. here, we compared the image filtering methods for abdomen CT scan and brain CT scan images and obtained results for filters namely Mean filter, Gaussian filter, Median filter and Minimum filter. By investigating the comparison parameters, it is clear that applying filters have different ability in removing noise (different in image quality parameters) from both abdomen and brain CT scan images and Median filter performs very well compare with the other filtering methods. The Median filter provides the maximum PSNR, SSIM and minimum MSE. The Median filter reduces noises in CT scan images for both abdomen and brain images.

- 50 - 5.3 Recommendations:

Performance evaluations of filters are still needed to evaluate ٠

the filter in removing noise from author medical image

modalities (MRI, US...).

Performance evaluation of filters in removing noise from CT ٠

images in pediatric patient.

Other different filters still needed to evaluate in removing ٠

noise from CT images.

Gaussian filter have ability in removing noise from both ٠

abdominal and brain CT scan images but can blurring edge

of the image.

Median filter have more ability in removing noise from both ٠

abdominal and brain CT scan images without blurring edge

of the image.

- 51 - 5.4 REFERENCES:

1. Prudhvi, V. N. & Venkateswarlu, T. (2012). Denoising of Medical Images using Total Variation Method. An International Journal (SIPIJ), 3(2), pp. 131 - 142. 2. R. C. Gonzalez and R.E. Woods, Digital Image Processing. 2nd ed. Englewood Cliffs, NJ: Prentice-Hall; 2002. 3. Dougherty, Geoff. Digital image processing for medical applications. Cambridge University Press, 2009. 4. Abdul Rehman & Wang, Z. (2012). Reduced-Reference Image Quality Assessment by Structural Similarity Estimation. IEEE Transactions on Image Processing, 21(8), pp. 3378 - 3389. 5. Thakur, Ishani, and Manish Kansal. "A Review on Noise Reduction from Medical Images." (2016). 6. Tiago Ferreira &Wayne Rasband. ImageJ User Guide IJ 1.46r. October, 2012. 7. Sudha, S., Suresh, G. R. & Suknesh, R. (2009). Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance. International Journal of Computer Theory and Engineering, 1(1), pp. 1793 - 8201. 8. Buades, A., Coll, B. & Morel, J. M. (2005). A Review of Image Denoising Algorithms, With a New One. Multiscale Modeling & Simulation, 4(2), pp. 490 - 530. 9. H. Barrett andW. Swindell. Radiological Imaging,Vols. I and II.Academic Press,NewYork, 1981. 10.S. Webb. From the Watching of Shadows: The Origins of Radiological Tomography. Institute of Physics, 1990.

- 52 - 11. H. P. Hiriyannaiah. X-ray computed tomography. IEEE Signal Process. Mag., 14(2):42-59, 1997. 12. Kaur, M. & Sharma, R. (2013). RESTORATION OF MEDICAL IMAGES USING DENOISING. International Journal for Science and Emerging .<35 - 38 Technologies with Latest Tends, 5(1),pv٠ 13. Al BOVIK. The Essential Guide to Image Processing ,2009. 14. Kaur, M. & Sharma, R. (2013). RESTORATION OF MEDICAL IMAGES USING DENOISING. International Journal for Science and Emerging Technologies with Latest Trends, 5(1), pp. 35 - 38. 15. A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall; 1989. 16. JingdongChen, JacobBenesty, Yiteng Huang, Simon Doclo, New Insights Into the Noise Reduction Wiener Filter,IEEE Transactions on audio, speech, and language processing, vol. 14, no. 4, July 2006. 17. R.L. Eubank. Spline Smoothing and Nonparametric Regression. Marcel Dekker, New York, second edition edition, 1988. 18. Brunet, D., Vrscay, E. R. & Wang, Z. (2011). A Class of Image Metrics Based on the Structural Similarity Quality Index. International Conference on Image Analysis and Recognition (ICIAR11), Burnaby, BC, Canada, 1, pp. 100 - 101. 19. Baluja, Y., Mishra, S., Saini, T. S. & Pati, M.V. (2013). Frequency Domain Based Data Hiding Technique for Audio Signal. International Journal of Innovative Research in Science, Engineering and Technology, 2(5), pp. 1564 - 1569. 20. Goyal, M. & Sekhon, G. S. (2011). Hybrid Threshold Technique for Speckle Noise Reduction using wavelets for Grey scale images. International Journal of Image, Graphics and Signal Processing (IJIGSP), 2(2), pp. 620 - 625.

- 53 - 21. Napoleon, D. & Praneesh, M. (2013). Detection Of Brain Tumor Using Kernel Induced Possiblistic C-Means Clustering. International Journal of Computer & Organization Trends, 3(9), pp. 436 - 438. 22. Ke Lu, Ning He, & Liang Li. (2012). Nonlocal Means-Based Denoising for Medical Images. Computational and Mathematical Methods in Medicine, 2012, pp. 7.

- 54 - Appendix A

Image quality Measures MSE PSNR SSIM Linear filters Mean 13 37.02 0.96 Gaussian 18.39 35.51 0.92 Non-linear filters Median 6.717 39.89 0.97

Minimum 55.847 30.69 0.78

Image 1

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.97 10.636 37.89702 Gaussian 0.93 16.04 36.11276 Non-linear filters Median 0.98 4.255 41.8758 Minimum 0.80 48.574 31.30076 Images 2

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9722 10.929 37.779 Gaussian 0.9391 16.309 36.04053 Non-linear filters Median 0.989 4.5 41.63267 Minimum 0.8104 51.728 31.02754 Images 3

- 55 - Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9718 8.757 38.74125 Gaussian 0.9449 11.463 37.57182 Non-linear filters Median 0.987 3.961 42.18675 Minimum 0.8322 33.298 32.94062 Images 4

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9603 11.588 37.52471 Gaussian 0.9244 17.013 35.85699 Non-linear filters Median 0.963 6.518 40.02366 Minimum 0.7535 56.335 30.65702 Images 5

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9627 8.288 38.9803 Gaussian 0.9353 11.539 37.54312 Non-linear filters Median 0.9622 5.189 41.01396 Minimum 0.8612 48.911 31.27073 Images 6

- 56 - Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9726 10.408 37.99113 Gaussian 0.9431 15.282 36.323 Non-linear filters Median 0.9885 4.357 41.77292 Minimum 0.8125 42.774 31.853 Images 7

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9696 9.375 38.44509 Gaussian 0.9392 14.035 36.69268 Non-linear filters Median 0.9863 4.651 41.48934 Minimum 0.8294 45.853 31.55112 Images 8

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9627 8.288 38.9803 Gaussian 0.9353 11.539 37.54312 Non-linear filters Median 0.9622 5.189 41.01396 Minimum 0.8612 48.911 31.27073 Images 9

- 57 - Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9747 11.018 37.74377 Gaussian 0.9449 16.257 36.0544 Non-linear filters Median 0.9903 4.012 42.13119 Minimum 0.8135 45.771 31.5589 Images 10

- 58 - Appendix B

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.986 5.837 40.5029 Gaussian 0.9663 9.537 38.37068 Non-linear filters Median 0.9941 1.798 45.6169 Minimum 0.899 30.924 33.26184 Image 1

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9703 14.61 36.5183 Gaussian 0.9332 21.737 34.7928 Non-linear filters Median 0.9878 5.335 40.89346 Minimum 0.7791 47.211 31.42437 Image2

- 59 - Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9894 5.634 40.65663 Gaussian 0.9708 10.027 38.15309 Non-linear filters Median 0.9971 1.896 45.38642 Minimum 0.8886 28.631 33.59643 Image 3

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9907 5.053 41.12931 Gaussian 0.9769 8.643 38.79815 Non-linear filters Median 0.998 0.822 49.01608 Minimum 0.9143 21.061 34.93001 Image 4

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9917 5.381 40.85617 Gaussian 0.9782 8.878 38.68165 Non-linear filters Median 0.9986 0.702 49.70143 Minimum 0.918 19.126 35.34856 Image 5

- 60 - Image quality Measures MSE PSNR SSIM Linear filters Mean 0.989 5.788 40.53951 Gaussian 0.9719 8.95 38.64657 Non-linear filters Median 0.9962 1.622 46.06429 Minimum 0.9098 23.97 34.36812 Image 6

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9726 13.635 36.81825 Gaussian 0.9377 21.182 34.90513 Non-linear filters Median 0.9857 4.609 41.52873 Minimum 0.7855 49.117 31.25248 Image 7

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9892 6.103 40.30937 Gaussian 0.9701 10.528 37.94134 Non-linear filters Median 0.9974 2.061 45.02402 Minimum 0.8831 29.538 33.46099 Image 8

- 61 - Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9717 16.435 36.0071 Gaussian 0.9162 22.775 34.59022 Non-linear filters Median 0.9717 8.272 38.98869 Minimum 0.7734 56.75 30.62514 Image 9

Image quality Measures MSE PSNR SSIM Linear filters Mean 0.9888 6.699 39.9047 Gaussian 0.9715 11.036 37.73668 Non-linear filters Median 0.9975 2.002 45.15016 Minimum 0.8963 27.913 33.70673 Image 10

- 62 - Appendix C

Pixels values Measures Area Mean SD Min Max Original 262144 107.361 87.853 0 255 Linear filters Mean 262144 107.358 86.824 0 255 Gaussian 262144 107.35 86.097 0 255 Non-linear Median 262144 107.002 87.563 0 255 filters Minimum 262144 97.501 83.781 0 255 Image 1

Pixels values Measures Area Mean SD Min Max Original 262144 102.804 84.182 0 255 Linear filters Mean 262144 102.805 83.153 0 255 Gaussian 262144 102.794 82.413 0 255 Non-linear Median 262144 102.527 83.852 0 255 filters Minimum 262144 93.942 80.025 0 255 Image 2

Pixels values Measures Area Mean SD Min Max Original 262144 99.717 83.700 0 255 Linear filters Mean 262144 99.71 82.679 0 255 Gaussian 262144 99.708 81.955 0 255 Non-linear Median 262144 99.465 83.359 0 255 filters Minimum 262144 90.705 79.029 0 255 Image 3

- 63 - Pixels values Measures Area Mean SD Min Max Original 262144 103.147 85.497 0 255 Linear filters Mean 262144 103.148 84.616 0 255 Gaussian 262144 103.137 84.07 0 255 Non-linear Median 262144 102.85 85.2 0 255 filters Minimum 262144 96.318 82.581 0 255 Image 4

Pixels values Measures Area Mean SD Min Max Original 262144 36.230 67.288 0 255 Linear filters Mean 262144 36.235 65.771 0 255 Gaussian 262144 36.228 64.694 0 255 Non-linear Median 262144 35.963 67.059 0 255 filters Minimum 262144 26.827 57.31 0 255 Image 5

Pixels values Measures Area Mean SD Min Max Original 262144 40.277 59.538 0 255 Linear filters Mean 262144 40.276 58.282 0 255 Gaussian 262144 40.255 57.636 0 255 Non-linear Median 262144 40.004 59.046 0 255 filters Minimum 262144 33.385 50.736 0 255 Image 6

- 64 - Pixels values Measures Area Mean SD Min Max Original 262144 90.897 77.948 0 255 Linear filters Mean 262144 90.892 76.804 0 255 Gaussian 262144 90.882 75.995 0 255 Non-linear Median 262144 90.598 77.579 0 255 filters Minimum 262144 82.622 72.813 0 255 Image 7

Pixels values Measures Area Mean SD Min Max Original 262144 112.735 86.521 0 255 Linear filters Mean 262144 112.736 85.65 0 255 Gaussian 262144 112.718 85.09 0 255 Non-linear Median 262144 112.458 86.225 0 255 filters Minimum 262144 104.775 83.176 0 255 Image 8

Pixels values Measures Area Mean SD Min Max Original 262144 102.032 86.399 0 255 Linear filters Mean 262144 102.037 85.788 0 255 Gaussian 262144 102.017 85.393 0 255 Non-linear Median 262144 101.859 86.247 0 255 filters Minimum 262144 96.322 84.032 0 255 Image 9

- 65 - Pixels values Measures Area Mean SD Min Max Original 262144 104.243 85.206 0 255 Linear filters Mean 262144 104.239 84.09 0 255 Gaussian 262144 104.229 83.26 0 255 Non-linear Median 262144 103.983 84.941 0 255 filters Minimum 262144 95.402 81.203 0 255 Image 10

- 66 - Appendix D

Pixels values Measures Area Mean SD Min Max Original 262144 45.373 61.471 0 255 Linear filters Mean 262144 45.376 60.772 0 255 Gaussian 262144 45.374 60.238 0 255 Non-linear Median 262144 45.376 61.363 0 255 filters Minimum 262144 40.043 53.946 0 255 Image 1

Pixels values Measures Area Mean SD Min Max Original 262144 69.887 104.627 0 255 Linear filters Mean 262144 40.276 58.282 0 255 Gaussian 262144 40.255 57.636 0 255 Non-linear Median 262144 40.004 59.046 0 255 filters Minimum 262144 33.385 50.736 0 255 Image 2

Pixels values Measures Area Mean SD Min Max Original 262144 35.796 54.331 0 255 Linear filters Mean 262144 35.801 53.39 0 255 Gaussian 262144 35.796 52.609 0 255 Non-linear Median 262144 35.694 53.988 0 255 filters Minimum 262144 30.447 47.163 0 255 Image 3

- 67 - Pixels values Measures Area Mean SD Min Max Original 262144 45.862 63.686 0 255 Linear filters Mean 262144 45.861 62.953 0 255 Gaussian 262144 45.86 62.353 0 255 Non-linear Median 262144 45.815 63.561 0 255 filters Minimum 262144 41.545 57.372 0 255 Image 4

Pixels values Measures Area Mean SD Min Max Original 262144 41.238 63.593 0 255 Linear filters Mean 262144 41.239 62.695 0 255 Gaussian 262144 41.24 61.996 0 255 Non-linear Median 262144 41.197 63.476 0 255 filters Minimum 262144 36.877 56.868 0 255 Image 5

Pixels values Measures Area Mean SD Min Max Original 262144 32.315 56.660 0 255 Linear filters Mean 262144 32.32 55.737 0 255 Gaussian 262144 32.312 55.019 0 255 Non-linear Median 262144 32.306 56.494 0 255 filters Minimum 262144 27.464 48.112 0 255 Image 6

- 68 - Pixels values Measures Area Mean SD Min Max Original 262144 74.883 106.011 0 255 Linear filters Mean 262144 74.885 103.734 0 255 Gaussian 262144 74.885 102.197 0 255 Non-linear Median 262144 74.772 105.822 0 255 filters Minimum 262144 62.245 99.045 0 255 Image 7

Pixels values Measures Area Mean SD Min Max Original 262144 36.302 55.906 0 255 Linear filters Mean 262144 36.305 54.886 0 255 Gaussian 262144 36.302 54.053 0 255 Non-linear Median 262144 36.168 55.512 0 255 filters Minimum 262144 30.701 48.228 0 255 Image 8

Pixels values Measures Area Mean SD Min Max Original 262144 53.603 87.597 0 255 Linear filters Mean 262144 53.607 84.825 0 255 Gaussian 262144 53.602 83.198 0 255 Non-linear Median 262144 53.431 87.201 0 255 filters Minimum 262144 40.267 75.648 0 255 Image 9

- 69 - Pixels values Measures Area Mean SD Min Max Original 262144 48.557 67.590 0 255 Linear filters Mean 262144 48.554 66.734 0 255 Gaussian 262144 48.555 66.048 0 255 Non-linear Median 262144 48.507 67.353 0 255 filters Minimum 262144 42.979 60.699 0 255 Image 10

- 70 - Appendix E

Image 2

- 71 - Image 3

Image 4

- 72 - Image 5

- 73 - Image 7

Image 8

- 74 - Image 10

- 75 - Appendix F

Image 1

Image 2

- 76 - Image 3

Image 4

- 77 - Image 6

- 78 - It?

Image 8

- 79 - Image 9

Image 10

- 80 -