Detection of hard exudates from diabetic retinopathy images using fuzzy logic. Issue 2 (1st March 2013)

• Record Type: Journal Article
• Authors: Ranamuka, Nayomi Geethanjali; Meegama, Ravinda Gayan N.
• Is Part Of: IET image processing. Volume 7:Issue 2(2013)
• Page Start: 121
• Page End: 130
• Publication Date: 2013-03-01
• Subjects: biomedical optical imaging -- blood vessels -- diseases -- eye -- fuzzy logic -- image colour analysis -- laser applications in medicine -- mathematical morphology -- medical image processing -- vision defects; hard exudate detection -- diabetic retinopathy images -- blood vessels -- human retina -- diabetic patients -- ophthalmology -- laser treatment -- patient treatment -- morphological image processing -- DR retinal images -- mathematical morphology -- optic disc -- adaptive fuzzy logic algorithm -- RGB colour space -- red channels -- green channels -- blue channels; biomedical optical imaging -- blood vessels -- diseases -- eye -- fuzzy logic -- image colour analysis -- laser applications in medicine -- mathematical morphology -- medical image processing -- vision defects; hard exudate detection -- diabetic retinopathy images -- blood vessels -- human retina -- diabetic patients -- ophthalmology -- laser treatment -- patient treatment -- morphological image processing -- DR retinal images -- mathematical morphology -- optic disc -- adaptive fuzzy logic algorithm -- RGB colour space -- red channels -- green channels -- blue channels; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2012.0134
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Diabetic retinopathy (DR), that affects the blood vessels of the human retina, is considered to be the most serious complication prevalent among diabetic patients. If detected successfully at an early stage, the ophthalmologist would be able to treat the patients by advanced laser treatment to prevent total blindness. In this study, the authors propose a technique based on morphological image processing and fuzzy logic to detect hard exudates from DR retinal images. At the initial stage, the exudates are identified using mathematical morphology that includes elimination of the optic disc. Subsequently, hard exudates are extracted using an adaptive fuzzy logic algorithm that uses values in the RGB colour space of retinal image to form fuzzy sets and membership functions. The fuzzy output for all the pixels in every exudate is calculated for a given input set corresponding to red, green and blue channels of a pixel in an exudate. This fuzzy output is computed for hard exudates according to the proportion of the area of the hard exudates. By comparing the results with hand‐drawn ground truths, the authors obtained sensitivity and specificity of detecting hard exudates as 75.43 and 99.99%, respectively.