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Title: Extraction of Features from Fundus Images for Glaucoma Assessment
Keywords: glaucoma, feature extraction, segmentation, optic disc, optic cup, diagnosis
Issue Date: 12-Aug-2011
Citation: YIN FENGSHOU (2011-08-12). Extraction of Features from Fundus Images for Glaucoma Assessment. ScholarBank@NUS Repository.
Abstract: Digital color fundus imaging is a popular imaging modality for the diagnosis of retinal diseases, such as diabetic retinopathy, age-related macula degeneration and glaucoma. Early detection of glaucoma can be achieved through analyzing features in fundus images. The optic cup-to-disc ratio and peripapillary atrophy (PPA) are believed to be strongly related to glaucoma. Glaucomatous patients tend to have larger cup-to-disc ratios, and more likely to have beta type PPA. Therefore, automated methods that can accurately detect the optic disc, optic cup and PPA are highly desirable in order to design a computer aided diagnosis (CAD) system for glaucoma. In this work, a novel statistical deformable model is proposed for optic disc segmentation. A knowledge-based Circular Hough Transform is utilized to initialize the model. In addition, a novel optimal channel selection scheme is proposed to enhance the segmentation performance. This algorithm is extended to the optic cup segmentation, which is a more challenging task. The PPA detection is accomplished by a regional profile analysis method, and the subsequent segmentation is achieved through a texture-based clustering scheme. Experimental results show that the proposed approaches can achieve a high correlation with the ground truth and thus demonstrate a good potential for these algorithms to be used in medical applications.
Appears in Collections:Master's Theses (Open)

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