Open Access

ARTICLE

An Automated Brain Image Analysis System for Brain Cancer using Shearlets

R. Muthaiyan^1,*, Dr M. Malleswaran²

1 Department of Electronics and Communication Engineering, University College of Engineering Thirukkuvalai, Tamilnadu, 610204, India
2 Department of Electronics and Communication Engineering, University College of Engineering Kancheepuram, Kancheepuram, 631552, India

* Corresponding Author: R. Muthaiyan. Email:

Computer Systems Science and Engineering 2022, 40(1), 299-312. https://doi.org/10.32604/csse.2022.018034

Received 22 February 2021; Accepted 27 April 2021; Issue published 26 August 2021

Abstract

In this paper, an Automated Brain Image Analysis (ABIA) system that classifies the Magnetic Resonance Imaging (MRI) of human brain is presented. The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis. The Non-Subsampled Shearlet Transform (NSST) that captures more visual information than conventional wavelet transforms is employed for feature extraction. As the feature space of NSST is very high, a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies. A combination of features that includes Gray Level Co-occurrence Matrix (GLCM) based features, Histograms of Positive Shearlet Coefficients (HPSC), and Histograms of Negative Shearlet Coefficients (HNSC) are estimated. The combined feature set is utilized in the classification phase where a hybrid approach is designed with three classifiers; k-Nearest Neighbor (kNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers. The output of individual trained classifiers for a testing input is hybridized to take a final decision. The quantitative results of ABIA system on Repository of Molecular Brain Neoplasia Data (REMBRANDT) database show the overall improved performance in comparison with a single classifier model with accuracy of 99% for normal/abnormal classification and 98% for low and high risk classification.

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Keywords

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