Latent factor model based classification for detecting abnormalities in retinal images

Abnormality detection in medical images is a critical problem across image modalities and organs. Many approaches to automatic abnormality detection use discriminative methods, based on domain knowledge, to address the problem. In this paper, we investigate the effectiveness of a generative model with no assumption of domain knowledge. We propose a method for classification of tissues based on Latent Factor analysis, and demonstrate it on colour retinal images with Diabetic Retinopathy-related abnormalities. A generative model based on Gaussian latent dictionaries is used to model various structures present at a patch level in an image. The model is used to classify a given patch into one of 5 classes: namely dark and bright lesions, neo-vascularisation (NV), plain tissue background and background with vessel. Evaluation of the proposed method was done on 3 different datasets. Same and cross-dataset validation of the method yields an area under the receiver operating characteristic curve (AUC) of 0.85 for abnormality detection. The method was also modified to address a challenging problem of NV detection by posing it as a 2-class problem and the AUC for the same was found to be 0.92. This establishes the potential of LF model for abnormality detection.