An Incremental Feature Extraction Framework for Referable Diabetic Retinopathy Detection

Diabetic retinopathy (DR) might be characterized by the occurrence of lesions in the retinal image. Existing approaches require a large set of retinal images where lesions in the image are individually annotated to learn a model that will classify an image as referable or non-referable DR. However, annotating individual lesions is a tedious task and the accuracy of the learnt model is limited by the availability of these annotated images. In this paper, we first learn a universal Gaussian mixture model (GMM) from a small set of annotated images. This universal GMM is then applied as the prior belief to learn an adaptive GMM for individual images. The proposed approach aims to capture the characteristics of referable versus non-referable images by examining the difference between the universal GMM and the adaptive GMM. An image-level classifier is then built based on these differences as features. Experimental results on three fundus image datasets (MESSIDOR, DIARETDB1 and SORC) indicate that the proposed framework achieves 92.1%, 97.68% and 87.1% ROC area values respectively. This approach also opens up a way to use the widely available public fundus images, where the images are labelled but not annotated, for progressively refining the universal GMM leading to an improved performance of approximately 5% and 1% respectively for SORC and MESSIDOR dataset after five refinement steps.