Automatic computer vision-based detection and quantitative analysis of indicative parameters for grading of diabetic ret
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S.I. : ADVANCES IN BIO-INSPIRED INTELLIGENT SYSTEMS
Automatic computer vision-based detection and quantitative analysis of indicative parameters for grading of diabetic retinopathy Ashish Issac1 • Malay Kishore Dutta1 • Carlos M. Travieso2 Received: 19 December 2017 / Accepted: 16 March 2018 Ó The Natural Computing Applications Forum 2018
Abstract Diabetic retinopathy (DR) is one of the complications of diabetes affecting the eyes. If not treated at an early stage, then it can cause permanent blindness. The present work proposes a method for automatic detection of pathologies that are indicative parameters for DR and use them strategically in a framework to grade the severity of the disease. The bright lesions are highlighted using a normalization process followed by anisotropic diffusion and intensity threshold for detection of lesions which makes the algorithm robust to correctly reject false positives. SVM-based classifier is used to reject false positives using 10 distinct feature types. Red lesions are accurately detected from a shade-corrected green channel image, followed by morphological flood filling and regional minima operations. The rejection of false positives using geometrical features makes the system less complex and computationally efficient. A comprehensive quantitative analysis to grade the severity of the disease has resulted in an average sensitivity of 92.85 and 86.03% on DIARETDB1 and MESSIDOR databases, respectively. Keywords Fundus images Diabetic retinopathy Optic disc Bright lesions Red lesions Mathematical morphology Classification Grading
1 Introduction Diabetic retinopathy (DR) is a disease related to retina of the human eye. This disease can cause permanent blindness to the affected person if proper treatment is not granted at an early stage. In developing countries, there is a scarcity of trained ophthalmologists and lack of awareness about such diseases. However, if proper awareness creation camps and some automated tools are developed, then some
& Malay Kishore Dutta [email protected] Ashish Issac [email protected] Carlos M. Travieso [email protected] 1
Department of Electronics and Communication Engineering, Amity University, Noida, India
2
Signals and Communication Department, IDeTIC, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
initial care can be given to patients and the progression of the disease can be delayed. The disease is mainly characterized by the presence of lesions, either bright or red, on the retina, venous beading and neo-vascularization. However, with the help of image processing, the visual changes observed in the retina can be detected and some algorithms can be developed to detect these abnormalities. Development of computer-aided tools for detection of DR has been critically important considering the lack of awareness and scarcity of ophthalmologists. Various lesions show changes in colour, geometrical features and texture features, and detection can be ma
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