Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology.

Computational and Mathematical Methods in Medicine
Feng TianWei Chen

Abstract

An improved blood vessel segmentation algorithm on the basis of traditional Frangi filtering and the mathematical morphological method was proposed to solve the low accuracy of automatic blood vessel segmentation of fundus retinal images and high complexity of algorithms. First, a global enhanced image was generated by using the contrast-limited adaptive histogram equalization algorithm of the retinal image. An improved Frangi Hessian model was constructed by introducing the scale equivalence factor and eigenvector direction angle of the Hessian matrix into the traditional Frangi filtering algorithm to enhance blood vessels of the global enhanced image. Next, noise interferences surrounding small blood vessels were eliminated through the improved mathematical morphological method. Then, blood vessels were segmented using the Otsu threshold method. The improved algorithm was tested by the public DRIVE and STARE data sets. According to the test results, the average segmentation accuracy, sensitivity, and specificity of retinal images in DRIVE and STARE are 95.54%, 69.42%, and 98.02% and 94.92%, 70.19%, and 97.71%, respectively. The improved algorithm achieved high average segmentation accuracy and low complexity while promising s...Continue Reading

References

Jan 6, 2007·Medical Image Analysis·M Elena Martinez-PerezKim H Parker
Jan 1, 1989·IEEE Transactions on Medical Imaging·S ChaudhuriM Goldbaum
Apr 17, 2016·Computer Methods and Programs in Biomedicine·Nagendra Pratap Singh, Rajeev Srivastava
Dec 7, 2018·Computer Methods and Programs in Biomedicine·Yanhui GuoAbdulkadir Şengür
Apr 20, 2019·Computers in Biology and Medicine·Dengqiang Jia, Xiahai Zhuang

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