MIRank-KNN: multiple-instance retrieval of clinically relevant diabetic retinopathy images.

Abstract

Diabetic retinopathy (DR) is a consequence of diabetes and is the leading cause of blindness among 18- to 65-year-old adults. Regular screening is critical to early detection and treatment of DR. Computer-aided diagnosis has the potential of improving the practice in DR screening or diagnosis. An automated and unsupervised approach for retrieving clinically relevant images from a set of previously diagnosed fundus camera images for improving the efficiency of screening and diagnosis of DR is presented. Considering that DR lesions are often localized, we propose a multiclass multiple-instance framework for the retrieval task. Considering the special visual properties of DR images, we develop a feature space of a modified color correlogram appended with statistics of steerable Gaussian filter responses selected by fast radial symmetric transform points. Experiments with real DR images collected from five different datasets demonstrate that the proposed approach is able to outperform existing methods.