An Automated Segmentation at Retinal Images for Use in Diabetic Retinopathy Studies
| dc.contributor.advisor | Assefa, Dawit (PhD.) | |
| dc.contributor.author | Moges, Daniel | |
| dc.date.accessioned | 2018-06-11T08:53:05Z | |
| dc.date.accessioned | 2023-11-04T15:22:13Z | |
| dc.date.available | 2018-06-11T08:53:05Z | |
| dc.date.available | 2023-11-04T15:22:13Z | |
| dc.date.issued | 2014-10 | |
| dc.description.abstract | Automated computer aided detection of retinal lesions associated with Diabetic Retinopathy (DR) offers many potential benefits. In a screening setting, it allows the examination of large number of images in less time and more objectively than traditional observer driven techniques. In a clinical setting, it can be an important diagnostic aid by reducing the workload of trained graders and other costs. However, the segmentation of major pathological structures and their subsequent follow–ups are not easy because of various artifacts such as presence of anatomical structures with highly correlated pixels with that of lesion, illumination variability, noise and movement of the eye during multiple visits by the patient. This study presents a novel mathematical scheme for analysis of color retinal images acquired through digital fundus cameras from patients treated for DR. The proposed scheme uses a holistic representation of the color images in the three (trinion) space and applies trinion based Fourier transforms to extract useful imaging features for the purpose of classification and segmentation of retinal images. A suitable color space transformation and a way of extracting robust higher order features are included in the method. The scheme has been applied in analyzing images acquired from standard retinal image databases. Results have showed that the algorithm achieved 86.06% sensitivity, 96.06% specificity, and 92.65% accuracy for pixel base segmentation of Hard Exudates (HEs) the most prevalent lesions that appears in the earliest stages of DR, while it achieved 96.67% sensitivity, 100% specificity and 97.3% accuracy for image base classification of abnormalities due to DR. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/309 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Automated; computer | en_US |
| dc.title | An Automated Segmentation at Retinal Images for Use in Diabetic Retinopathy Studies | en_US |
| dc.type | Thesis | en_US |