An automated computer-aided diagnosis system for classification of MR images using texture features and gbest-guided gravitational search algorithm

The segmentation and classification of brain magnetic resonance (MR) images are the crucial and challenging task for radiologists. The conventional methods for analyzing brain images are time-consuming and ineffective in decision-making. Thus, to overcome these limitations, this work proposes an aut...

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Published inBiocybernetics and biomedical engineering Vol. 40; no. 2; pp. 815 - 835
Main Authors Shanker, Ravi, Bhattacharya, Mahua
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.04.2020
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ISSN0208-5216
DOI10.1016/j.bbe.2020.03.003

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Abstract The segmentation and classification of brain magnetic resonance (MR) images are the crucial and challenging task for radiologists. The conventional methods for analyzing brain images are time-consuming and ineffective in decision-making. Thus, to overcome these limitations, this work proposes an automated and robust computer-aided diagnosis (CAD) system for accurate classification of normal and abnormal brain MR images. The proposed CAD system has the ability to assist the radiologists for diagnosis of brain MR images at an early stage of abnormality. Here, to improve the quality of images before their segmentation, contrast limited adaptive histogram equalization (CLAHE) is employed. The segmentation of the region of interest is obtained using the multilevel Otsu's thresholding algorithm. In addition, the proposed system selects the most significant and relevant features from the texture and multiresolution features. The multiresolution features are extracted using discrete wavelet transform (DWT), stationary wavelet transform (SWT), and fast discrete curvelet transform (FDCT). Moreover, the Tamura and local binary pattern (LBP) are used to extract the texture features from the images. These features are used to classify the brain MR images using feedforward neural network (FNN) classifier, where different meta-heuristic optimization algorithms, e.g., genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), and gbest-guided gravitational search algorithm (GG-GSA) are employed for optimizing the weights and biases of FNN. The extensive experimental results on DS-195, DS-180, and three standard datasets show that the classification accuracy of GG-GSA based FNN classifier outperforms all mentioned meta-heuristic-based classifiers and several state-of-the-art methods.
AbstractList The segmentation and classification of brain magnetic resonance (MR) images are the crucial and challenging task for radiologists. The conventional methods for analyzing brain images are time-consuming and ineffective in decision-making. Thus, to overcome these limitations, this work proposes an automated and robust computer-aided diagnosis (CAD) system for accurate classification of normal and abnormal brain MR images. The proposed CAD system has the ability to assist the radiologists for diagnosis of brain MR images at an early stage of abnormality. Here, to improve the quality of images before their segmentation, contrast limited adaptive histogram equalization (CLAHE) is employed. The segmentation of the region of interest is obtained using the multilevel Otsu's thresholding algorithm. In addition, the proposed system selects the most significant and relevant features from the texture and multiresolution features. The multiresolution features are extracted using discrete wavelet transform (DWT), stationary wavelet transform (SWT), and fast discrete curvelet transform (FDCT). Moreover, the Tamura and local binary pattern (LBP) are used to extract the texture features from the images. These features are used to classify the brain MR images using feedforward neural network (FNN) classifier, where different meta-heuristic optimization algorithms, e.g., genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), and gbest-guided gravitational search algorithm (GG-GSA) are employed for optimizing the weights and biases of FNN. The extensive experimental results on DS-195, DS-180, and three standard datasets show that the classification accuracy of GG-GSA based FNN classifier outperforms all mentioned meta-heuristic-based classifiers and several state-of-the-art methods.
Author Bhattacharya, Mahua
Shanker, Ravi
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Keywords Computer-aided diagnosis system
Multilevel Otsu's thresholding
Magnetic resonance imaging
Gbest-guided gravitational search algorithm
Meta-heuristic optimization algorithms
Texture and multiresolution features
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Snippet The segmentation and classification of brain magnetic resonance (MR) images are the crucial and challenging task for radiologists. The conventional methods for...
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SubjectTerms Computer-aided diagnosis system
Gbest-guided gravitational search algorithm
Magnetic resonance imaging
Meta-heuristic optimization algorithms
Multilevel Otsu's thresholding
Texture and multiresolution features
Title An automated computer-aided diagnosis system for classification of MR images using texture features and gbest-guided gravitational search algorithm
URI https://www.clinicalkey.com/#!/content/1-s2.0-S0208521620300401
https://dx.doi.org/10.1016/j.bbe.2020.03.003
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