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

• Autorzy: Shanker Ravi , Bhattacharya Mahua

Identyfikatory

DOI

10.1016/j.bbe.2020.03.003

• Języki publikacji: EN

Abstrakty

EN

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 limita-tions, 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 experimen-tal 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.

Słowa kluczowe

EN

magnetic resonance imaging; multilevel Otsu's thresholding; texture features; multiresolution features; meta heuristic optimization algorithm; gbest guided gravitational search algorithm; computer-aided diagnosis system

PL

rezonans magnetyczny; cechy teksturalne; algorytm optymalizacji; diagnoza wspomagana komputerowo

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 2
• Strony: 815--835
• Opis fizyczny: Bibliogr. 70 poz., rys., tab., wykr.

Twórcy

autor

Shanker Ravi

• Department of ICT, ABV-Indian Institute of Information Technology and Management, Gwalior 474015, Madhya Pradesh, India

autor

Bhattacharya Mahua

• Department of ICT, ABV-Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).