Open AccessBinary operation based hard exudate detection and fuzzy based classification in diabetic retinal fundus images for real time diagnosis applications
Author(s) -
Arun Pradeep,
X. Felix Joseph
Publication year - 2020
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v10i3.pp2305-2312
Subject(s) - computer science , artificial intelligence , diabetic retinopathy , fuzzy logic , binary number , pattern recognition (psychology) , rgb color model , fundus (uterus) , set (abstract data type) , computer vision , binary image , sensitivity (control systems) , image processing , image (mathematics) , mathematics , medicine , ophthalmology , arithmetic , engineering , electronic engineering , programming language , diabetes mellitus , endocrinology
Diabetic retinopathy (DR) is one of the most considerable reasons for visual impairment. The main objective of this paper is to automatically detect and recognize DR lesions like hard exudates, as it helps in diagnosing and screening of the disease. Here, binary operation based image processing for detecting lesions and fuzzy logic based extraction of hard exudates on diabetic retinal images are discused. In the initial stage, the binary operations are used to identify the exudates. Similarly, the RGB channel space of the DR image is used to create fuzzy sets and membership functions for extracting the exudates. The membership directives obtained from the fuzzy rule set are used to detect the grade of exudates. In order to evaluate the proposed approach, experiment tests are carriedout on various set of images and the results are verified. From the experiment results, the sensitivity obtained is 98.10%, specificity is 96.96% and accuracy is 98.2%. These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR.