Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image Recuperation Approach
Retinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/534045 |
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| author | K. Somasundaram P. Alli Rajendran |
| author_facet | K. Somasundaram P. Alli Rajendran |
| author_sort | K. Somasundaram |
| collection | DOAJ |
| description | Retinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true positive ratio of exudates detection, respectively. These mechanically detected exudates did not include more detailed feature selection technique to the system for detection of diabetic retinopathy. To categorize the exudates, Diabetic Fundus Image Recuperation (DFIR) method based on sliding window approach is developed in this work to select the features of optic cup in digital retinal fundus images. The DFIR feature selection uses collection of sliding windows with varying range to obtain the features based on the histogram value using Group Sparsity Nonoverlapping Function. Using support vector model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy disease level. The ranking of disease level on each candidate set provides a much promising result for developing practically automated and assisted diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, ranking efficiency, and feature selection time. |
| format | Article |
| id | doaj-art-5dcaa7dd31a14fe083550d2a8eaf8c2f |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-5dcaa7dd31a14fe083550d2a8eaf8c2f2025-08-20T03:34:44ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/534045534045Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image Recuperation ApproachK. Somasundaram0P. Alli Rajendran1Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul 624 622, IndiaDepartment of Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai, Tamil Nadu 625 009, IndiaRetinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true positive ratio of exudates detection, respectively. These mechanically detected exudates did not include more detailed feature selection technique to the system for detection of diabetic retinopathy. To categorize the exudates, Diabetic Fundus Image Recuperation (DFIR) method based on sliding window approach is developed in this work to select the features of optic cup in digital retinal fundus images. The DFIR feature selection uses collection of sliding windows with varying range to obtain the features based on the histogram value using Group Sparsity Nonoverlapping Function. Using support vector model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy disease level. The ranking of disease level on each candidate set provides a much promising result for developing practically automated and assisted diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, ranking efficiency, and feature selection time.http://dx.doi.org/10.1155/2015/534045 |
| spellingShingle | K. Somasundaram P. Alli Rajendran Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image Recuperation Approach The Scientific World Journal |
| title | Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image Recuperation Approach |
| title_full | Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image Recuperation Approach |
| title_fullStr | Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image Recuperation Approach |
| title_full_unstemmed | Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image Recuperation Approach |
| title_short | Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image Recuperation Approach |
| title_sort | diagnosing and ranking retinopathy disease level using diabetic fundus image recuperation approach |
| url | http://dx.doi.org/10.1155/2015/534045 |
| work_keys_str_mv | AT ksomasundaram diagnosingandrankingretinopathydiseaselevelusingdiabeticfundusimagerecuperationapproach AT pallirajendran diagnosingandrankingretinopathydiseaselevelusingdiabeticfundusimagerecuperationapproach |