Contrast Limited Adaptive Histogram Equalization Lab Analysis
L={116*(Y/Yn) for (Y/Yn)>0.008856 903.3*(Y/Yn); Otherwise a = 500*(f(X/Xn –f(Y-Yn)) b = 200*(f(X/Xn –f(Z-Zn)) where Xn ,Yn and Zn are the tristimulus values of the reference white. A median filter is applied on the L band in order to preserve edges and to reduce noise . A Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is used. To enhance the contrast and the separability between exudates and the background , a Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is used which consists of subdividing the image into non-overlapping rectangular regions and then applying their local equalization histogram.
B. Blood vessel Detection and Elimination The exact detection of artery and …show more content…
4. List of features measured for each centerline pixel
C.AVR Calculation and Disease Detection Early retinal vessel caliber changes may predict risk of diabetic retinopathy. These can be determined by calculating the arteriolar-to-venular diameter ratio(AVR) [17]-[19]. For normal retinal images the AVR ratio varies from 2/3 to 3/4.As diabetic retinopathy progresses, the vein becomes wider and the AVR ratio get decreased. Based on the AVR value disease detection is made and if it is found to be affected then further processing are required for the estimation of the stage of the disease. D. Extraction of Exudate region
The bright intensity regions in the fundus images with distinct boundaries is considered as the hard exudate. K means clustering algorithm is applied to extract these regions [20]. The clustering algorithm have been tried for different values of k and the best result is obtained for k = 6. The papillary region and other yellow lesions, such as cotton wool spots/ soft exudates are detected, because of their similar attributes to hard exudates in terms of brightness, color and contrast
B. Blood vessel Detection and Elimination The exact detection of artery and …show more content…
4. List of features measured for each centerline pixel
C.AVR Calculation and Disease Detection Early retinal vessel caliber changes may predict risk of diabetic retinopathy. These can be determined by calculating the arteriolar-to-venular diameter ratio(AVR) [17]-[19]. For normal retinal images the AVR ratio varies from 2/3 to 3/4.As diabetic retinopathy progresses, the vein becomes wider and the AVR ratio get decreased. Based on the AVR value disease detection is made and if it is found to be affected then further processing are required for the estimation of the stage of the disease. D. Extraction of Exudate region
The bright intensity regions in the fundus images with distinct boundaries is considered as the hard exudate. K means clustering algorithm is applied to extract these regions [20]. The clustering algorithm have been tried for different values of k and the best result is obtained for k = 6. The papillary region and other yellow lesions, such as cotton wool spots/ soft exudates are detected, because of their similar attributes to hard exudates in terms of brightness, color and contrast