Umapathy Eaganathan* et al. /International Journal of Pharmacy & Technology

ISSN: 0975-766X CODEN: IJPTFI Available Online through Research Article www.ijptonline.com ANALYSIS OF VARIOUS COLOR MODELS IN MEDICAL IMAGE PROCESSING Umapathy Eaganathan* Faculty in Computing, Asia Pacific University, Bukit Jalil, Kuala Lumpur, 57000, Malaysia.

Received on: 20.10.2016 Accepted on: 25.11.2016

Abstract

Color is a visual element for humanbeing to perceive in everyday activities. Inspecting the color manually may be a tedious job to the humanbeing whereas the current digital systems and technologies help to inspect even the pixels color accurately for the given objects. Various researches carried out by the researchers over the world to identify diseased spots, damaged spots, pattern recognition, and graphical identification. Color plays a vital role in image processing and geographical information systems. This article explains about different color models such as RGB,

YCbCr, HSV, HSI, CIELab in medical image processing and it additionally explores how these color models can be derived from the basic color model RGB. Further this study will also discuss the suitable color models that can be used in medical image processing.

Keywords : Image Processing, Medical Images, RGB Color, Image Segmentation, Pattern Recognition, Color

Models

1. Introduction

Nowadays in medical health system the images playing a vital role throughout the process of medical disease management, intensive treatment, surgical procedures and clinical researches. In medical image processing such as neuro-imaging, bio-imaging, medical visualisations the imaging modalities based on digital images and may vary depends upon the diagnosis stage. Main improvements of medical imaging systems working faster than the traditional disease identification system. The current disease identification systems based on image processing following pixel resolution and diseases can be fetched easily. During the last decade the amount of image data captured in various forms like Color image Red Green and Blue, Grey image and BW image. By the way the color images in general stored in the form of Red, Green and Blue color space (RGB). However the color spaces exist such as YCbCr, HSI,

HSV and CIELab color space available for image application development. Most algorithms developed and described

IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22926-22936 Page 22926 the color features based on computer vision. In this paper different color models will be described rely on medical image processing and it contributing even in the agriculture field the image processing helping in a wider way to identify diseases in the plant and in the leaves. This article explains some of the important color spaces frequently used in color image processing. This is followed by a review of selected related literature.

1.1 Color models

A color image is a combination of some basic colors. In MATLAB breaks each individual pixel of a color image termed as „true color‟ down into RGB values. Nowadays images normally captured by using color equipments like digital cameras, mobile phones, video devices and other hand held devices. By capturing the photos with the help of these equipments the captured image may be in clear format, sometimes the taken images covered with noises.

Umapathy Eaganathan, Tel: +60123010953, Fax: +603-8996-1001

So it may be like colored images, low resolution images, web based colored images, graphical colored images or other format of images. To extract the color features from the content of a leaf image, a suitable color space followed with its descriptor. The main objective of color space is to facilitate the specification of colors. Various color spaces available in digital image processing such as RGB, HSI, HSV, CIE L*a*b, CIE L*u*v, have been developed for attain goal. Therefore, the RGB color space, a widely used system for representing color images and most commonly used method to represent color feature of an image is the color histogram [1].

1.1.1 RGB Color model

RGB color is specified with the fundamental of colors. A normalized color histogram is used to detect the pixels of leaf color of an image in RGB color space. It is the basic color model and other color models are derived from it and it is light sensitive [2]. This color model is extensively used in color space for storing and processing the digital images. The combinations of these three colors join together to derive an end products called as RGB. This is one of the most popularly chosen and used color in all the systems through computer graphics and the dimensional system as follows.

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In the cube the black is at the origin and the white is at the opposite end of the cube. The gray line follows from black to white. In general a color graphics system follows 8 bits per color channel in a 24 bit color graphics system, red is

(255,0,0) and on the color cube it is (1,0,0) [3].

1.1.2 YCbCr Color model

This specified in terms of lumninance (Y channel) and chrominance (Cb and Cr channels). It divides the image into a luminous component and chrominance components. The distribution of the leaf areas is consistent across different in

Cb and Cr color spaces. The difference between blue and luma is represented by Cb and the difference between red and luma component is denoted by Cr.

Y = 0.299R + 0.587G + 0.114B

Cr = R-Y, Cb = B-Y

The similarity between RGB and the YCbCr color space is luminance independent. Further it helps and given better performance [4]. YCbCr color space is used for component digital video is a scaled and offset version of the YUV color space. This color model is the basic color model used in analogue color TV broadcasting. The principal advantage of the YUV model in image processing is decoupling of luminance and color information. Whenever the conversion take place from RGB to YCbCr that should be divided the Cb and Cr. The Y is define to have range from

16-235, Cb and Cr are defined to have a nominal range from 16-240 [5].

1.1.3 HSV Color model

HSV color is specified in terms of Hue (H), Saturation (S) and Value (V) are three components. This color model is widely used to describe color perceived by human being and the intensity value is represented by V. The transformation of RGB color model to HSV color model can be derived by the following with ranges of (0,1) [6]. r = R / (R+G+B) g = G / (R+G+ B) b = B / (R+G+B)

Let MAX = maximum of (r,g,b) values and

MIN = minimum of those values then,

Rˈ =

Gˈ =

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Bˈ =

S =

V = MAX

Hue refers to the color of red, blue and yellow and has the range of 0 to 360. Saturation refers purity of the color and takes the value from 0 to 100%.

Value refers the brightness of the color and provides the achromatic idea of the color [7]. So the hue (H), saturation or color purity (S), and the brightness (V) spaces are very useful and extended in many applications of image analysis and computer graphics [8].

1.1.4 HSI color model

One of the most attractive colors in image processing applications is HSI. Because it represents color similarity that can be easily identified by human eyes to sense the colors. In this model I is the intensity component, S is the saturation component that is the degree of white color embedded in specific color and H is the hue component that measures color purity.

The HSI family of color models uses cylindrical coordinates for the representation of RGB points. This color model also known as HSL, where L represents the lightness [9]. Most of the applications recently used the HSI color model.

An Intensity images can be processed in various image processing applications such as histogram operations, intensity transformations and convolutions. Hence the performance of these operations done effectively by using HSI color space.

H =

I =

1.1.5 CIE Lab

It is one of the perceptual uniform color spaces, and it shown in the following that how two colors differ in human observation. The identical color spaces were defined and arranged by the perceptual difference of the colors. In these color spaces, the computation of the luminance(L) and the chroma (ab or uv) is obtained through a non-linear

IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22926-22936 Page 22929 mapping of the XYZ coordinates. In CIE Lab or CIELab there are three components exist namely the first component

L represent luma that is illumination information and ab represent the chroma information. L*=0 gives the black color and L*=100 gives white color. The a*, the values a*<0 indicate green while the values a*>0 indicate magenta.

The b*, the values b*<0 indicate blue and values b*>0 indicate yellow [4].

II. Related studies in medical image processing

Raghu et.al. (2007) [10] discussed about an algorithmic approach to predict diseases in retinal images. Authors explained some novel ideas and algorithms for systematically process the retinal images included color based segmentation with prewitt, sobel operators. Here the RGB vector spaces used for color representation.

Straightforward method implemented interms of color slicing then defined by further processing. The methods proposed for edge detection used morphological image processing followed by gradient operation for focal vessel narrowing. For extracting of veins this research followed bottom hat transformation. In this method the concept explained that how the structural element transforms the image to the required boundary image. The another method followed using eroding for extraction of veins. In the prediction algorithm the images were processed and arranged in required format then cotton wool spots were detected and quantized whenever it presented. Simultaneously hemorrhages detected and quantized by color segmentation. The next step established was veins extraction using bottom hat transformation. Hence the comparison made between these counts against the patient‟s database. Hence discussed that image processing techniques helping identification of diseases and incorporated with all health care system, provides adequate education of the patient. Further the authors suggested related algorithms can help to detect cardiac diseases and provide quantitative results and also supports the medical experts.

Haifei et.al. (2011) [11] described about the method for extracting defects in capsules by using color image processing. The chosen color space was RGB and built an environment to directly segment the capsule defects

IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22926-22936 Page 22930 instead of replacing HSI color space. Here the first step followed was removed the background and extraction the capsules. Before the process of color segmentation this article followed grayscale image with its interpretation of

HSI colors. Further established a relationship between the three color components in RGB color space. The capsule segmented directly without converting it to other segmentation HSI space. The region growing method followed to separate two regions of defect and the capsule. In capsule extraction the background was removed before extraction of capsule with interference of background with time. Analysis study based on color features done for each capsule and extracted separately. In the capsule defect segmentation algorithm this paper followed a 5 X 5 pixel template.

Hence this paper extracted the capsule by the three components relationship in RGB color. Further this research will be implemented as capsule defect identification system and also extended to industrial products defects identification.

Done-Sik et.al. (2014) [12] explored about an intuitive menu based image processing system for medical images.

This study described about a semi-automated system for segmentation of human organs and classifications. The high resolution model is necessary to analyse made use on human body by using telecommunication devices. This article also explained about the high resolution model by segmenting images like CT images and MRI followed the standard

RGB color. The problem identified from manual classification was difficult and time consuming task and human being may do mistakes while doing all the tasks. Here explained the factor of error rises in various levels and the error cannot be identified accurately.

This proposed method was designed to adopt an intuitive menus which was easier to understand by the users easily with independent. This semi-automated system was implemented with single thresholding, multi thresholding, histogram equalization, region growing, morphology operations, color selections for tissue classification also followed. Authors implemented this system also to perform 3D segmentation, job can be continued without choosing processing parameters then images can be displayed in an independent window. Color selection was added by using basic color model. Hence the anatomical structures segmented and classified easily by people in an independent types of images.

Bong-hyun et.al. (2008) [13] explained a diagnosis method for heart diseases using face color. Authors discussed how the cardiovascular diseases risen due to the environmental changes and other several causes in Korea. This article proposed methodology that conducted color compensation to extract the region of facial, nose, mouth, ear, eyes and the target system. In additionally the authors discussed about the heart can be referred to the king of all organs, it can be said that whether our body can work hard or not, can be strong. The established studies of facial area

IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22926-22936 Page 22931 have been used for biological identification. This paper also focused on objectification and visualization which can extract the regions even the analysis also still important based on color extraction. Here authors referred both two colors such as RGB and white color. Here the process followed conversion from RGB values to YCbCr values. The median filter used to search arbitrary size and filtering the pixels by the median values. The region color analysis was carried out to shown the parts of identified color pixel values. Luis et.al. (2013) [14] implemented a system for

Alzheimer‟s disease detection and classification in MRI images by using RGB color for different type of patients.

The outline of this project methodology followed wavelet feature extraction with dimensionality reduction and classification was performed using SVM (Support Vector Machines) and NN (Neural Networks). Authors made study about how the Alzheimer causes human memory loss, symptoms of the disease. In additionally the authors suggested the early diagnosis for this disease. The existing study was introduced about Principal Component Analysis

(PCA) as feature reduction algorithms and Neural Networks for best classifications. However most of the articles published intelligent classification system Alzheimer‟s disease used limited number of input for both training and test strategies. But the proposed article compared the use of feature reduction methods in the classification of that disease with larger database as well as different divisions followed in training set and test set. T-test analysis performed in the execution with discrete wavelet transform used for feature extraction in identification. Hence the authors proven RGB color space is suitable for the diagnosis.

Yu-Hsiang et.al. (2010) [15] explored about automatic liver disease diagnosis using generalized Hough transform for early disease detection diagnosis based on the images. In this paper the authors elaborated an automatic liver cirrhosis diagnosis system using adaptive ultrasound image matching. The first step was preprocessing module with filter speckle noise and detect stable edge pattern done by Hough transform. The other steps were detection of ROI, color feature extraction and selection then classification performed in this article. Many approaches has been followed such as wavelet transform, median filter and cubic-spline interpolation in the related study to filter the speckle noise from the given ultrasound images. The color space followed here RGB and authors provided block diagram for the proposed system which involved the classification phase and training phase. In the training phase the elimination of noise from speckle template followed Gaussian filter then thresholding the smooth template at the end comparing the edge pattern with Generalized Hough transform. The classification procedure followed the edge pattern template to map the edge pattern query image with ROI. Hence this article produced enhanced quality of clinical ultrasound images, stable edge pattern and automatic ROI detection. Yiwei et.al. (2009) [16] presented about clinical image

IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22926-22936 Page 22932 processing algorithm based on color analysis. This paper explained about self-adaptive color analysis edge tracking method relies on shape. The traditional endoscopes system used optical endoscope to focus site due to controlled visual angle and magnification. In various clinical images like as Xray, MRI images, CT images and combination of microscope slice images helped doctors in olden days to identify the disease for the treatment analysis. So the uses of computer graphics technology efficiently processed the improvement of image quality with high valuable information. Problems identified in the clinical diagnosis the images were collected using endoscope by sensors then converted those images into digital signals and then finally transmit that signal into image acquisition card. Here the authors implemented a new algorithm for color analysis image based on Red, Green and Blue with edge tracking arithmetic have been used to show automatically the image edge feature point quickly and effectively. The algorithm taken step of input images, format conversion, noise filtering, binary regional image, locate initial characteristics point, edge tracking, calculate damage area then derived results. The three colors RGB mixed because computer systems use RGB values to identify easily.

Zoltan et.al. (2011) [17] developed a software about identification of colon cancer in tissue images with this patients can get information about early diagnosis. Authors followed an algorithm called color structure code and it performed color based segmentation. The process carried out in the existing study was preprocessing contained thresholding and noise filtering. The watershed algorithm was used for nuclei segmentation. This paper proposed two necessary module for the diagnosis, one for nuclei detection and next is gland detection. The HSV and LAB color space was used in gland detection and connected components were used for identifying glands independent objects. Here the morphological operation was used to generate the connected components. Next step followed into separation and identification of tissue in order to connect pixel sets based on sequential connected component analysis. The real glands found using color based segmentation and some non-glands also detected well. Few nuclei also found in some regions to avoid non-detection of glands. Authors decided to convert the color images into HSV color space for nucleus detection. Additionally for analysing the efficiency of developed system often used measures of information retrieval such as true positive, true negative, false positive and false negative. Hence this project was developed based on color based approach with an advance of detection in shape, position and size of glands.

Aldo et.al. (2014) [18] explored the consistency and standardization of color in medical imaging. This artcle summarized the consensus summit on color in medical imaging at the food and drug administration. This summit was discussed and reviewed about how the color is currently handled by medical imaging systems and also identified the

IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22926-22936 Page 22933 areas need to be improved in medical for an better facilitation of key areas. Authors provided literature study about the previous analysis in various color medical imaging for diagnostic purposes. The color standardization and consistency is achieved in this paper with the help of variety of color critical imaging applications. Simultaneously the current practices and challenges of color imaging also discussed with variety of medical disciplines noticeable color differences in the identification process. In display systems visualization of color medical images as evidenced by color calibration. Images compared and studied related to RGB color space for color reproduction.

Conclusion

Image analysis is one of the important steps to identify the problems in the medical diagnosis. The development of image analysis is characterised the colors used in medical image processing. In the related literature view most of the researches followed Red Green Blue color space for the disease diagnosis in the medical field. The defects or the diseases can be identified by the image shape and also with feature. The main objectives of color can be used to identify the diseases easily by naked eyes and in image processing system also. So far various studies related to the medical image processing discussed above for the enhancement of future research will be carried out easily.

Acknowledgment

The author would like to say heartfelt thanks to the Dean School of Computing and Head of School and Academic

Leader for their constant support and encouragement by the Asia Pacific University, Malaysia support for publishing this article in this International Conference.

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