Blood vessel segmentation from fundus image by a cascade classification framework

Highlights：

• We propose a novel and robust cascade classification framework for retinal vessel segmentation. This classification model 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.

• The proposed cascade classification framework produces a definite training and testing result that can be exactly reproduced. This greatly reduces the uncertainty of its usage in the both training and testing and hence it is user friendly.

• We show that the proposed cascade classification framework can handle typically challenging retinal vessel structures that are difficult segmentation issues for some state-of-the art methods.

• As an adaptive and effective solution to the difficult classification problems, the proposed technique can be flexibly extended to other image recognition tasks.