CIKITUSI JOURNAL FOR MULTIDISCIPLINARY RESEARCH ISSN NO: 0975-6876
DIGITAL CIELUV COLOR IMAGE SEGMENTATION
Dr.Muntha Raju1, Dr.Ganesh Mani2
1,2Professor, Department of Computer Science & Engineering Shadan College of Engineering & Technology Hyderabad, Telengana, India. Email:[email protected] [email protected]
Abstract: The identification of retinal blood vessels is very important but crucial task to analyze the severity of the retinal diseases such as diabetic retinopathy, macular degeneration, central retinal vein occlusion, central retinal artery occlusion, retinal detachment and branch retinal vein occlusion. It is evident that huge number of computer based automated algorithms are developed for the accurate detection of blood vessels and optical disc. Most of the work utilizes the retinal fundus images in RGB color space. The proposed work implements the detection and segmentation of retinal blood vessel in RGB and device independent CIELUV color space. The proposed work for the segmentation retinal blood vessel is based on adaptive histogram equalization, median filtering and morphological operations.
Keywords: Segmentation, Retinal Blood Vessel, Adaptive Histogram Equalization, Median Filtering, Mathematical Morphology
1. INTRODUCTION Retinal blood vessel detection and segmentation is a vital process for the precise illustration, analysis, diagnosis, planning of early treatment and surgery for retinal diseases such as diabetic retinopathy, macular degeneration, central retinal vein occlusion, central retinal artery occlusion, retinal detachment and branch retinal vein occlusion1-3. Recent years, due to the advancement of latest technologies, a huge number of automated methods developed for the segmentation of blood vessels from retinal color fundus images4-6. However, identification and segmentation of retinal blood vessels still remains an exigent assignment due to the abnormalities, non-uniform illumination, varying shape and size of the vessels, and anatomical variability between subjects5. Number of works presented innovative methods 21-27 for the automatic detection and segmentation of retinal vessels ns in color fundus images. The proposed work implements the detection and segmentation of retinal blood vessel in RGB and device independent CIELUV color space. A color space is nothing but a method or way of creating and visualizing colors9. Human eye describes color as three important attributes of hue, brightness and colorfulness10. But a computer monitor define color as the percentage of red (R), green (G), and blue (B) phosphor emissions11. Various color spaces had developed for different applications13. The input image taken from image sensor is usually in RGB color space. However, this device dependent and non uniform color space is not suitable for objects identification and recognition of colors12. Moreover, it is very difficult to find out an exact color in RGB color space. So it is very important to transform RGB color image into other color spaces such as CIELuv14. The main advantage of CIELuv color space is that it is device independent. i.e., the same color information is displayed irrespective of equipment18. CIELuv color space is uniformly derived from CIEXYZ color space17. In perceptual uniform space, such as CIELuv, any two colors those are equally far-away in the color space are equally distant perceptually15. In CIELuv color space, the component L indicates the actual visual difference and the color information (red/blue and yellow/blue) are stored in u and v components16.
2. PROPOSED SYSTEM
Figure 2 illustrates the proposed system for the detection and segmentation of retinal blood vessel in color fundus images. The proposed approach is explained as follows.
Step 1: The input image is acquired from the DRIVE date base (https://www.isi.uu.nl/Research/Databases/DRIVE/).
The DRIVE database is extensively used by the researcher to facilitate the relative investigations on segmentation of retinal blood vessels and assessment their algorithms on this database20. The information
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incorporated in this database can be used, free of charge, for research and educational purposes. The photographs for the DRIVE database were obtained from a diabetic retinopathy screening program in The Netherlands. The screening population consisted of 400 diabetic subjects between 25-90 years of age. Each image has been JPEG compressed. Step 2: The input image is enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE)
The CLAHE algorithm partitions the images into contextual regions and applies the histogram equalization to each one. This evens out the distribution of used grey values and thus makes hidden features of the image more visible. The full grey spectrum is used to express the image. While histogram equalization works on the entire image, CLAHE operates on small regions in the image, called tiles. Each tile's contrast is enhanced, so that the histogram of the output region approximately matches a specified histogram. After performing the equalization, CLAHE combines neighboring tiles using bilinear interpolation to eliminate artificially induced boundaries. To avoid amplifying any noise that might be present in the image, CLAHE to limit the contrast, especially in homogeneous areas.
Step 3: The enhanced color image is transformed into gray scale (intensity) image.
The gray-scale image contains all the details of information; it is easy for understanding and has not ambiguities typical of black-and-white images. For many applications of image processing, color information doesn't help us identify important edges or other features
Fig.1 Representation of CIELuv color space
Input Image Vessel Detection
Enhancement Morphological
Operations
Intensity Image Edge Detection
Filtering
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Fig.2 Proposed system for the detection and segmentation of retinal blood vessel in color fundus images
Step 4: The enhanced intensity image is filtered using a median filter of 3*3 mask.
The median filter is a simple edge-preserving smoothing filter. It may be applied prior to segmentation in order to reduce the amount of noise in a stack of 2D images19. The filter works by sorting pixels covered by a NxN mask according to their grey value. The center pixel is then replaced by the median of these pixels, i.e., the middle value. Figure 3 illustrates an example for median filtering.
Fig.3 An example for median filtering
Step 5: Edge detection performed by canny edge detector
The canny edge detector is a multistage edge recognition procedure to identify an extensive range of edges in images. The various steps involved in canny edge detection is explained as follows.
Preprocessing: Smoothing (blurring) of the image to remove the unnecessary noisy pixels and unwanted details and textures7-8. Usually Gaussian filter (mask) of size 5 * 5 is utilized for this purpose. Smooth the image with a Gaussian filter to reduce noise and unwanted details and textures.
( ) ( ) ( ) (1)
Where ( ) = Input image and ( ) = Gaussian filter
{ } (2) √
Gradient Computation: Gradient magnitudes and directions are calculated at every single point in the image gradient operators such as Roberts, Prewitt, etc. The magnitude of the gradient at a point determines if it possibly lies on an edge or not. A high gradient magnitude means the colors are changing rapidly - implying an edge. A low gradient implies no substantial changes. So it's not an edge. The magnitude and angle of the gradient is represented in (3) and (4) respectively.
√ (3)
( ) (4)
Suppression of Non-Maximum: Only local maxima should be marked as edges i.e., if a pixel is not a maximum, it is suppressed.
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Thresholding: Prospective edges are found out using two threshold values - an upper threshold and a lower threshold. The upper threshold is utilized to mark edges that are potential edges. The lower threshold is to find faint pixels that are actually a part of an edge.
Linking potential edges to form continuous edges: final edges are determined by suppressing all edges that are not connected to a very certain (strong) edge.
Step 6: Morphological opening operation is applied to the edge detected output image.
Morphological opening is the composite operation of erosion followed by dilation. For dilation operation on binary images, the output pixel is set to 1 if any of the pixels is set to the value 1. Similarly, for erosion operation the output pixel is set to 0 if any of the pixels is set to 0. Opening smoothest the inside of the object contour, brakes narrow strips and eliminates thin portion of the image. The opening operation is used to remove noise and CCD detected in the image.
Step 7: The outcome is the segmented image which is the clear representation of the retinal blood vessels. 3. EXPERIMENTAL AND RESULTS Figure 4 illustrates the end result of the proposed approach for the detection of retinal blood vessels in RGB color space. The input color fundus image and its enhanced version are depicted in 4(a) and 4(b) respectively. The input image is enhanced by Contrast Limited Adaptive Histogram Equalization.
(a) Input image (b) Enhanced image (c) Intensity Image
(d) Median filtered image (e) Canny edge detection (f) Morphological Operation
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(g) Output Image
Fig.4 Outcome of the proposed method in RGB color space
The gray-scale image contains all the details of information. For many applications of image processing, color information doesn't help us identify important edges or other features. The gray scale image is shown in fig 4(c). The enhanced intensity image is filtered using a median filter of 3*3 mask which is illustrated in fig 4(d). The canny edge detector is a multistage edge recognition procedure to identify an extensive range of edges in images. Figure 4(e) portrayed the edges of the fundus image. Morphological opening is the composite operation of erosion followed by dilation. Morphological opening operation is applied to the edge detected output image. The outcome of the process is illustrated in fig 4(f). Figure 4(g) represents the segmented image which is the clear representation of the retinal blood vessels.
(a) Enhanced CIELuv image (b) Luminance (L) Image (c) Median filtered image
(d) Canny edge detection (e) Morphological Operation (f) Output Image
Fig.5 Outcome of the proposed method in CIELuv color space
Figure 5 demonstrated the outcome of the proposed approach for the detection of retinal blood vessels in CIELuv color space. The enhanced color fundus image in CIELuv color space and its luminance channel (L) are
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depicted in 5(a) and 5(b) respectively. The input image is enhanced by Contrast Limited Adaptive Histogram Equalization. The enhanced intensity image is filtered using a median filter of 3*3 mask which is illustrated in fig 5(c). Figure 5(d) portrayed the edges of the fundus image. Morphological opening is the composite operation of erosion followed by dilation. Morphological opening operation is applied to the edge detected output image. The outcome of the process is illustrated in fig 5(e). Figure 5(f) represents the segmented image which is the clear representation of the retinal blood vessels.
4. CONCLUSION The proposed work implements the detection and segmentation of retinal blood vessel in RGB and device independent CIELUV color space. The proposed work for the segmentation retinal blood vessel is based on adaptive histogram equalization, median filtering and morphological operations. The identification of retinal blood vessels is very important but crucial task to analyze the severity of the retinal diseases such as diabetic retinopathy, macular degeneration, central retinal vein occlusion, central retinal artery occlusion, retinal detachment and branch retinal vein occlusion. The investigational consequences show the efficiency of the proposed method.
ETHICS AND CONSENT: This article does not contain any studies with human participants or animals performed by any of the authors. No direct participation of human entertains in this article.
CONFLICT OF INTEREST: We are declaring that, there is no conflict of interest regarding the publication of this paper.
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