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|Title:||Automated microaneurysm segmentation and detection using generalized eigenvectors||Authors:||Pallawala, P.M.D.S.
|Issue Date:||2007||Citation:||Pallawala, P.M.D.S.,Hsu, W.,Lee, M.L.,Goh, S.S. (2007). Automated microaneurysm segmentation and detection using generalized eigenvectors. Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005 : 322-327. ScholarBank@NUS Repository. https://doi.org/10.1109/ACVMOT.2005.26||Abstract:||Diabetic retinopathy is a major cause of blindness and microaneurysms are the first clinically observable manifestations of diabetic retinopathy. Regular screening and timely intervention can halt or reverse the progression of this disease. This paper describes an approach that is based on the generalized eigenvectors of affinity matrix to extract microaneurysms from digital retinal images. Microaneurysms are in the low intensity regions and detection is complicated by their small sizes, the presence of retinal vessels, and their similarity to another type of retinal abnormality - haemorrhages. In order to accurately detect microaneurysms, the affinity matrix is defined to suppress larger structures such as blood vessels, haemorrhages, etc and to create uniform affinity distribution for pixels belonging to microaneurysms. The generalized eigenvector solution seeks to find the optimal segmentation for microaneurysms and provides indication to the possible locations of microaneurysms. We differentiate the true microaneurysms by studying their feature characteristics. Experiments on 70 retinal sub-images of diabetic, patients indicate that we are able to achieve 93% accuracy in the detection of microaneurysms.||Source Title:||Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005||URI:||http://scholarbank.nus.edu.sg/handle/10635/43221||ISBN:||0769522718||DOI:||10.1109/ACVMOT.2005.26|
|Appears in Collections:||Staff Publications|
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