Automated assessment of diabetic retinopathy severity using content-based image retrieval in multimodal fundus photographs.

Purpose: Recent studies on diabetic retinopathy (DR) screening in fundus photographs suggest that disagreements between algorithms and clinicians are now comparable to disagreements among clinicians. The purpose of this study is to (1) determine whether this observation also holds for automated DR severity assessment algorithms, and (2) show the interest of such algorithms in clinical practice.

Methods: A dataset of 85 consecutive DR examinations (168 eyes, 1176 multimodal eye fundus photographs) was collected at Brest University Hospital (Brest, France).

Use of a JPEG-2000 Wavelet Compression Scheme for Content-Based Ophtalmologic Retinal Images Retrieval.

In this paper we propose a content based image retrieval method for diagnosis aid in diabetic retinopathy. We characterize images without extracting significant features, and use histograms obtained from the compressed images in JPEG-2000 wavelet scheme to build signatures. The research is carried out by calculating signature distances between the query and database images.