Multi-space clustering for segmentation of exudates in retinal color photographs.
Exudates are a class of lipid retinal lesions visible through optical fundus imaging, and indicative of diabetic retinopathy. We propose a clustering-based method to segment exudates, using multi-space clustering, and colorspace features. The method was evaluated on a set of 89 images from a publicly available dataset, and achieves an accuracy of 89.7% and positive predictive value of 87%.
Ram
Keerthi
K
Centre for Visual Information Technology, International Institute of Information Technology-Hyderabad, India. keerthiram@research.iiit.ac.in
Sivaswamy
Jayanthi
J
D000465:Algorithms; D001698:Biomedical Engineering; D016000:Cluster Analysis; D016208:Databases, Factual; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D003941:Diagnostic Techniques, Ophthalmological / Q000706:statistics & numerical data*; D005122:Exudates and Transudates; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data; D010781:Photography / Q000379:methods* / Q000706:statistics & numerical data; D012160:Retina / Q000473:pathology*