Design of Optimal Adaptive Filters for Two-Dimensional Filamentary Structures Segmentation

The problems of filamentary structures segmentation encompass retinal vessel detection in fundus images, reconstruction and tracing of neurons in microscopic images and segmentation of human vasculatures in two-dimensional digital angiography. In this letter, we focus on retinal vessel segmentation which is an important and challenging problem among others. Many retinal vessel segmentation algorithms have been developed, where most of them rely on the filtering technique. However, the available filters suffer from undesirable responses at some vessel and non-vessel structures. Thus, we propose a new framework based on optimal adaptive filters for retinal vessel segmentation. The filter coefficients are obtained by solving multiple inverse problems under a regularization framework. The performance of the proposed framework is evaluated on fundus images from the DRIVE and STARE datasets. In the experimental section, we show that the proposed framework outperforms all of the compared methods in four indicators namely sensitivity, F1-score, G-mean and Mathews correlation coefficient.