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22926-22936 Page 22926 the Color Features Based on Computer Vision 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. IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22926-22936 Page 22927 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 = IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 22926-22936 Page 22928 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.
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