Blood vessel segmentation from fundus image by a cascade classification framework

Accurate segmentation of retinal vessel from fundus image is a prerequisite for the computer-aided diagnosis of ophthalmology diseases. In this paper, we propose a novel and robust cascade classification framework for retinal vessel segmentation. Our classification model envelops a set of computationally efficient Mahalanobis distance classifiers to form a highly nonlinear decision. Different from other nonlinear classifiers that need a predefined nonlinear kernel or need iterative training, the proposed cascade classification framework is trained by a one-pass feed forward process. Thus, the degree of nonlinearity of the proposed classifier is not predefined, but determined by the complexity of the data structure. Experimental evaluations on three diverse publicly available databases show that the proposed cascade classification framework achieves 95.41–96.40% vessel segmentation accuracy, and outperforms the state-of-the-art methods in terms of F1-score and Matthew correlation coefficient consistently on all three diverse databases. A qualitative comparison between different segmentation approaches demonstrates the superiority of the proposed method in dealing with typically challenging retinal structures. The proposed cascade classification framework consistently yields a high performance for retinal vessel segmentation, and delineates a more complete and accurate vessel tree. As an adaptive and effective solution to the difficult classification problems, the proposed technique can be flexibly extended to other image recognition tasks.