Top-down and bottom-up strategies in lesion detection of background diabetic retinopathy

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 2; pp. 422 - 428 vol. 2
• Main Authors: Xiaohui Zhang, Chutatape, O.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Biomedical imaging; Blood vessels; Cotton; Diabetes; Hemorrhaging; Lesions; Lighting; Object detection; Retinopathy; Wool
• ISBN: 0769523722; 9780769523729
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2005.346

Bright lesions in the form of exudates and cotton wool spots while dark lesions consisting of hemorrhages are main evidences of background diabetic retinopathy that require early detection and precise classification. Based on different properties of bright lesions and dark lesions, bottom-up and top-down strategies are applied respectively to cope with the main difficulties in lesions detection such as inhomogeneous illumination. In bright lesion detection, a three-stage, bottom-up approach is applied. After local contrast enhancement preprocessing stage, two-step Improved Fuzzy C-Means is applied in Luv color space to segment candidate bright lesion areas. Finally, a hierarchical SVM classification structure is applied to classify bright non-lesion areas, exudates and cotton wool spots. In hemorrhage detection, a top-down strategy is adopted. The hemorrhages are located in the ROI firstly by calculating the evidence value of every pixel using SVM. Then their boundaries can be accurately segmented in the post-processing stage.