Unsupervised Segmentation of Blood Vessels from Colour Retinal Fundus Images
This paper represents an algorithm based on curvature evaluation and Entropy Filtering techniques with texture mapping for guideline.The method is used for the detection of blood vessels from colour retinal fundus images.In order to evaluate vessel-like patterns,segmentation is performed with respect to a precise model.We evaluate the curvature of blood vessels via carrying out eigenvalue analysis of Hessian matrix.This method allows to extract the fine retinal image ridge but introduces the effect of central light reflexes.We apply entropy filtering techniques to calculate the segmentations in relation to central reflex vessels.For efficient differentiation of vessels from analogous background patterns,we use spectral clustering to partition the image texture.It is an alternative of traditional intensity thresholding operation and allows more automatic processing of retinal vessel images.The detection algorithm that derives directly from this modeling is based on five steps: 1) image preprocessing; 2) curvature evaluation; 3) entropy filtering; 4) texture mapping; 5) morphology operation with application of vessel connectivity constraints.
retinal vasculature spectral clustering fundus image Sobel edge detection central light reflex entropy filtering morphological operation.
Xiao-Xia Yin Brian W.-H. Ng Jing He Yanchun Zhang Derek Abbott
Centre for Applied Informatics, College of Engineering and Science,Victoria University, Melbourne, V Centre for Biomedical Engineering and School of Electrical & Electronic Engineering, The University
国际会议
The Third International Coference on Health Information Science(HIS2014)2014年第三届健康信息学国际学术会议
深圳
英文
194-203
2014-04-22（万方平台首次上网日期，不代表论文的发表时间）