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). Two clinicians with different experience levels determined DR severity in each eye, according to the International Clinical Diabetic Retinopathy Disease Severity (ICDRS) scale. Based on Cohen's kappa (κ) measurements, the performance of clinicians at assessing DR severity was compared to the performance of state-of-the-art content-based image retrieval (CBIR) algorithms from our group. RESULTS: At assessing DR severity in each patient, intraobserver agreement was κ = 0.769 for the most experienced clinician. Interobserver agreement between clinicians was κ = 0.526. Interobserver agreement between the most experienced clinicians and the most advanced algorithm was κ = 0.592. Besides, the most advanced algorithm was often able to predict agreements and disagreements between clinicians. CONCLUSIONS: Automated DR severity assessment algorithms, trained to imitate experienced clinicians, can be used to predict when young clinicians would agree or disagree with their more experienced fellow members. Such algorithms may thus be used in clinical practice to help validate or invalidate their diagnoses. CBIR algorithms, in particular, may also be used for pooling diagnostic knowledge among peers, with applications in training and coordination of clinicians' prescriptions.

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. A weighted distance between histograms is used. Retrieval efficiency is given for different standard types of JPEG-2000 wavelets, and for different values of histogram weights. A classified diabetic retinopathy image database is built allowing algorithms tests. On this image database, results are promising: the retrieval efficiency is higher than 70% for some lesion types.