A novel retinal vessel extraction algorithm based on matched filtering and gradient vector flow

Lei Yu; Mingliang Xia; Li Xuan

doi:10.1117/12.2030264

27 October 2013 A novel retinal vessel extraction algorithm based on matched filtering and gradient vector flow

Lei Yu, Mingliang Xia, Li Xuan

Proceedings Volume 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing; 89200H (2013) https://doi.org/10.1117/12.2030264
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

Abstract

The microvasculature network of retina plays an important role in the study and diagnosis of retinal diseases (age-related macular degeneration and diabetic retinopathy for example). Although it is possible to noninvasively acquire high-resolution retinal images with modern retinal imaging technologies, non-uniform illumination, the low contrast of thin vessels and the background noises all make it difficult for diagnosis. In this paper, we introduce a novel retinal vessel extraction algorithm based on gradient vector flow and matched filtering to segment retinal vessels with different likelihood. Firstly, we use isotropic Gaussian kernel and adaptive histogram equalization to smooth and enhance the retinal images respectively. Secondly, a multi-scale matched filtering method is adopted to extract the retinal vessels. Then, the gradient vector flow algorithm is introduced to locate the edge of the retinal vessels. Finally, we combine the results of matched filtering method and gradient vector flow algorithm to extract the vessels at different likelihood levels. The experiments demonstrate that our algorithm is efficient and the intensities of vessel images exactly represent the likelihood of the vessels.

Citation Download Citation

Lei Yu, Mingliang Xia, and Li Xuan "A novel retinal vessel extraction algorithm based on matched filtering and gradient vector flow", Proc. SPIE 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing, 89200H (27 October 2013); https://doi.org/10.1117/12.2030264

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