Title: MR-brain image classification system based on SWT-LBP and ensemble of SVMs

Authors: Mohammed Khalil; Habib Ayad; Abdellah Adib

Addresses: LIM@II-FSTM, B.P. 146 Mohammedia 20650, Morocco ' LIM@II-FSTM, B.P. 146 Mohammedia 20650, Morocco ' LIM@II-FSTM, B.P. 146 Mohammedia 20650, Morocco

Abstract: In this paper, we present an efficient magnetic resonance (MR) image classification system. At the first stage, the brain image is decomposed into several subbands using stationary wavelet transform (SWT). Then, local binary patterns (LBP) with reduced histograms are computed for each subband to form several primary feature vectors. Principal components analysis (PCA) followed by linear discriminant analysis (LDA) are then applied to each primary feature vector in order to transform them into new lower-dimension feature vectors. The third stage consists of using an ensemble of support vector machines (SVMs) in order to build voters and make the final decision on the requested image. The designed system is evaluated on 255 brain images with five-fold cross-validation. Experimental results show that the proposed system achieves a classification rate of 99.78% which outperforms the existing brain classification approaches.

Keywords: MR-brain image classification; local binary patterns; LBP; stationary wavelet transform; SWT; linear discriminant analysis; LDA; ensemble of SVMs.

DOI: 10.1504/IJMEI.2021.113393

International Journal of Medical Engineering and Informatics, 2021 Vol.13 No.2, pp.129 - 142

Received: 08 Oct 2018
Accepted: 12 Feb 2019
Published online: 03 Mar 2021 *

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