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

Journal: PloS one
Published Date: Mar 6, 2020

Abstract

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.

Authors

  • Dharmateja Adapa
    Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering, College of Engineering, Shantou University, Shantou, Guangdong, China.
  • Alex Noel Joseph Raj
    Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China. jalexnoel@stu.edu.cn.
  • Sai Nikhil Alisetti
    Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering, College of Engineering, Shantou University, Shantou, Guangdong, China.
  • Zhemin Zhuang
    Engineering College, Shantou University, Shantou, Guangdong, China.
  • Ganesan K
    TIFAC-CORE, School of Electronics, Vellore Institute of Technology, Vellore, India.
  • Ganesh Naik
    MARCS Institute, Western Sydney University, Australia.

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