A SVM approach for detection of hemorrhages in background diabetic retinopathy

Hemorrhages are main evidences of background diabetic retinopathy and early detection is essential for an effective treatment. In this paper, a top-down strategy is applied to detect hemorrhages. After color normalization preprocessing stage, an evidence value for every pixel is calculated by SVM. The SVM classifier uses features extracted by combined 2DPCA instead of explicit image features as the input vector. After locating the hemorrhages in the ROI, the boundaries of the hemorrhages can be accurately segmented by the post-processing stage. The paper demonstrates a new implementation of various techniques on the problem and shows the improvement it offers over the others. Combined 2DPCA is proposed and virtual SVM is applied to achieve the higher accuracy of classification.