A supervised blood vessel segmentation technique for digital Fundus images using Zernike Moment based features

被引：46

作者：

Adapa, Dharmateja ^{[1
]}

Raj, Alex Noel Joseph ^{[1
]}

Alisetti, Sai Nikhil ^{[1
]}

Zhuang, Zhemin ^{[1
]}

Ganesan, K. ^{[2
]}

Naik, Ganesh ^{[3
]}

机构：

[1] Shantou Univ, Coll Engn, Dept Elect Engn, Key Lab Digital Signal & Image Proc Guangdong Pro, Shantou, Guangdong, Peoples R China

[2] Vellore Inst Technol, Sch Elect, TIFAC Core, Vellore, Tamil Nadu, India

[3] Western Sydney Univ, MARCS Inst, Sydney, NSW, Australia

来源：

PLOS ONE | 2020年 / 15卷 / 03期

关键词：

RETINAL IMAGES; MATCHED-FILTER; CLASSIFICATION; NETWORK;

D O I：

10.1371/journal.pone.0229831

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This paper proposes a new supervised method for blood vessel segmentation using Zernike moment-based shape descriptors. The method implements a pixel wise classification by computing a 11-D feature vector comprising of both statistical (gray-level) features and shape-based (Zernike moment) features. Also the feature set contains optimal coefficients of the Zernike Moments which were derived based on the maximum differentiability between the blood vessel and background pixels. A manually selected training points obtained from the training set of the DRIVE dataset, covering all possible manifestations were used for training the ANN-based binary classifier. The method was evaluated on unknown test samples of DRIVE and STARE databases and returned accuracies of 0.945 and 0.9486 respectively, outperforming other existing supervised learning methods. Further, the segmented outputs were able to cover thinner blood vessels better than previous methods, aiding in early detection of pathologies.

引用

页数：23

共 47 条

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