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In this paper we propose a novel method for automatic detection red lesions in digital fundus images. Candidate red lesions are extracted by a novel method called automatic seed generation (ASG). For classification, an implicitly hybrid classifier called spatio temporal feature map classifier (STFM) has been employed. Inclusion of a new feature called elliptic variance during classification phase...
In this paper we propose a novel method for automatic detection of microaneurysms (MA) and hemorrhages (HG)grouped as red lesions. Candidate extraction is achieved by automatic seed generation (ASG) which is devoid of morphological top hat transform (MTH). For classification we tested on linear discriminant classifier (LMSE), kNN, GMM, SVM and proposed a Hybrid classifier that incorporates kNN and...
The automatic screening of patients for early detection and prevention of diabetic retinopathy (DR) has been the prime focus in recent times due to the large ratio of patients to medical ophthalmologists. Exudate detection is one of the main steps of DR. A reliable method for detection of exudates is presented in this paper. Optic disc (OD) is localized by the principle component analysis (PCA). Active...
Accurate segmentation of optic disc (OD) in retinal images is of critical importance in diagnosis of diabetic retinopathy (DR). The accuracy of OD boundary detection using active contours is based on homogeneity of OD region. In this work we improve upon active contour model segmentation of OD from morphologically preprocessed fundus image in the Lab color space. The key contribution presented here...
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