Machine learning appraoch to automatic exudate detection in retinal images of diabetic patients | |
| Author | Yin Aye Moe |
| Call Number | AIT Thesis no.CS-07-02 |
| Subject(s) | Diabetic retinopathy Image processing--Digital techniques |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. CS-07-02 |
| Abstract | Diabetic Retinopathy (DR) is the commonest cause of vision loss in the world. Early detection and treatment of these diseases are crucial to avoid preventable vision loss and blindness. To lower the cost of detection, we employ machine learning approach to detect automatically the presence of abnormalities in the retinal images. The research in this thesis focuses on one of the abnormal signs that is, the presence of exudates, also called lesions, in the retinal images. This system examines the retinal images and presents only those containing exudates to the ophthalmologists. In this manner, the total workload of ophthalmologists can be reduced. In order to detect exudates, a three-stage approach is applied to detect and classify bright lesions. After preprocessing stage, Difference-of-Gaussian (DoG) filters are applied to extract the features of images. Finally, support vector machine (SYM) classifier is applied to classify exudates and nonexudates. |
| Year | 2007 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. CS-07-02 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Dailey, Matthew N.; |
| Examination Committee(s) | Guha, Sumanta;Bunyarit Uyyanonvara; |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship; |
| Degree | Thesis (M.Sc.) - Asian Institute of Technology, 2007 |