Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

Abstract EN

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community.

Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis.

In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL).

The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification.

The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs.

We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.