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Symmetry-based brain abnormality identification in Magnetic Resonance Images (MRI)

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Abstract

Medical image processing, which includes many applications such as magnetic resonance image (MRI) processing, is one of the most significant fields of computer-aided diagnostic (CAD) systems. It has witnessed great growth over the last few decades as a result of the tremendous advancements in computer technology. One of the applications that uses MRI and digital image processing techniques is to assess whether the brain has any anomalies. The large variation in the brain shape among people poses a significant challenge in the computer-based diagnosis process. As a result, comparing a person’s brain image to other people’s brain images may not be a reliable way to diagnose a brain tumour. In this study, we present a method that takes advantage of the fact that the two lobes of the brain are symmetric to decide if there are any abnormalities as tumours cause a deformation in the shape of one of the lobes, which affects this symmetry. The proposed method determines the status of the brain by comparing the two lobes of the brain with each other and decides the presence of abnormalities in it based on the results of the comparison. Various features extracted from the images, such as colour and texture, have been studied, discussed, and used in the comparison process. The proposed algorithm was applied to 300 images from standard datasets and the results obtained were very satisfactory where the precision, recall, and accuracy reached 95.3%, 94.7%, and 95% respectively. The obtained results and the limitations are thoroughly discussed and benchmarked with state-of-the-art approaches and the results of the evaluation are discussed as well.

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  1. Department of Computer Science and MIS, Oman College of Management and Technology, Muscat, Oman

    Mohammad A. N. Al-Azawi

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Correspondence to Mohammad A. N. Al-Azawi.

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Al-Azawi, M.A.N. Symmetry-based brain abnormality identification in Magnetic Resonance Images (MRI). Multimed Tools Appl 82, 2563–2586 (2023). https://doi.org/10.1007/s11042-022-12197-4

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