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

PloS One
Dharmateja AdapaGanesh Naik

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.

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Citations

Dec 29, 2020·International Journal of Imaging Systems and Technology·Deepika SelvarajAlex Noel Joseph Raj
Feb 11, 2021·Computer Methods and Programs in Biomedicine·Oscar Ramos-SotoRobert J Zawadzki

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Methods Mentioned

BETA
feature extraction

Software Mentioned

STARE
MATLAB
CLAHE
DRIVE
STARE ( STructured Analysis of the Retina )

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