13 March 2013 Automatic detection of retinal vascular bifurcations and crossovers based on isotropy and anisotropy
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86691T (2013) https://doi.org/10.1117/12.2006710
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
The analysis of retinal blood vessels is very important in the detection of some diseases in early stages, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease, and stroke. The bifurcations and crossovers are important feature points, which play important roles in the analysis of the retinal vessel tree. These feature points have been demonstrated to be important features in many visual tasks such as image registration, mosaicing, and segmentation. In this paper, a new method is proposed to detect vascular bifurcations and crossovers in fundus images. The Gaussian filter is applied to the blue channel of the original color retinal images to suppress the central reflex and reduce the candidate points. The eigenvalues and eigenvectors of Hessian matrix are then obtained in multiple scales to provide the structural and directional information. By computing the anisotropy and isotropy of neighboring image segments for each pixel in a retinal image, we define a multi-scale vessel filter which combines the responses of tubular structures and the responses of bifurcations and crossovers. Finally, the proposed method has been tested with publicly available database STARE and DRIVE. The experimental results show that bifurcations, crossovers and tubular structures can be detected simultaneously. And the proposed method performs well in detecting the bifurcations and crossovers which are in thin vessels or low contrast vessels.
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Guodong Li, Dehui Xiang, Fei Yang, Xiaonan Wan, Xin Yang, Jie Tian, "Automatic detection of retinal vascular bifurcations and crossovers based on isotropy and anisotropy", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86691T (13 March 2013); doi: 10.1117/12.2006710; https://doi.org/10.1117/12.2006710
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