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

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.

On the classification of simple and complex biological images using Krawtchouk moments and Generalized pseudo-Zernike moments: a case study with fly wing images and breast cancer mammograms

“… Liao & Pawlak (1995) , Bailey & Srinath (1996) ). In biology, they have been used in the analysis of complex biological images, for tasks like classification of cellular subtypes ( Ryabchykov et al, 2016 ), bacteria strains ( Bayraktar et al, 2006 ), ophthalmic pathologies ( Adapa et al, 2020 ), cancer cell phenotypes ( Alizadeh et al, 2016 ), breast cancer phenotypes ( Tahmasbi, Saki & Shokouhi, 2011 ; Narváez & Romero, 2012 ; Saki et al, 2013 ; Cordeiro, Santos & Silva-Filho, 2016 ), fingerprint identification ( Kaur & Pannu, 2019 ), and facial recognition ( Akhmedova & Liao, 2019 ).…”

An integrated feature frame work for automated segmentation of COVID‐19 infection from lung CT images

“…The shape descriptor is good to define the infected pixel information range from its background region. The shape parameters can be extracted using descriptor based on Zernike and Hu moments 47,48 . For better representation, the shape descriptor should possess the properties such as (a) provide low redundancy and higher discrimination ability.…”

A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

“…This method achieved an accuracy of 94.5% using the DRIVE dataset. Similarly, Adapa et al [15] used ANNs fed with an input vector based on Zernike moments. They obtained an overall accuracy of 94.5%.…”

“…These graylevel features may indicate the presence of blood vessels, but they do not contain any information about the shape of vessels. Moreover, they are sensitive to the fovea and the optic disc structures, which also are different from the background [15]. Hence, features describing the blood vessel's shape must provide additional information about the eye vasculature.…”

“…We used some shape descriptors invariant to translation, rotation, and scale to detect these shapes, which are different in shape and angle. The descriptors are known as Hu moments [14,26] and Zernike moments [15], and have effectively been used for representing shape features in other works. In this paper, we have taken the Hu moments, which are described below.…”