A Survey of Dictionary Learning in Medical Image Analysis and Its Application for Glaucoma Diagnosis

Abstract

Dictionary learning has shown its effectiveness in computer vision with the concise expression form but the powerful representation. Dictionary learning represents images with a bag of visual words (BoVW), which is a collection of atoms expressively representative for images. Recently, several task-specific dictionary learning methods have been proposed and successfully applied in medical image analysis, such as de-noising, classification, segmentation, and so on, which promotes the development of computer-aided diagnosis. In this paper, first we give a survey for dictionary learning-based medical image analysis methods including: (1) three discriminative dictionary learning frameworks, (2) CT image de-noising based on dictionary learning, and (3) histopathological image classification using sparse representation. Then, a novel method named Low-rank Shared Dictionary Learning (LRSDL), is presented to achieve accurate glaucoma diagnosis on fundus images. The LRSDL generates a shared codebook for image reconstruction and a particular one to handle the difference between the healthy and glaucomatous images. Benefit from this strategy, LRSDL not only possess distinct glaucoma-related features, but also share common patterns among all the fundus images. Experimental results show that the method effectively delivers glaucoma diagnosis with the accuracy of 92.90%. This endows dictionary learning method a great potential for glaucoma diagnosis and proves the feasibility of its application to medical image analysis.