Classification of magnetic resonance images for brain tumour detection. Issue 12 (9th September 2020)

• Record Type: Journal Article
• Authors: Kurmi, Yashwant; Chaurasia, Vijayshri
• Is Part Of: IET image processing. Volume 14:Issue 12(2020)
• Page Start: 2808
• Page End: 2818
• Publication Date: 2020-09-09
• Subjects: support vector machines -- medical image processing -- image segmentation -- tumours -- biomedical MRI -- multilayer perceptrons -- feature extraction -- image classification -- learning (artificial intelligence) -- brain; low‐level information -- linear filter -- higher‐level image features -- handcrafted feature extraction -- segmentation‐based localised region -- MRI datasets -- magnetic resonance image -- automatic lesion recognition method -- multistage image segmentation method -- automated brain tumour detection -- multilayer perceptron classifiers -- support vector machine -- Fisher vector -- disparity map; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1631
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Image segmentation of magnetic resonance image (MRI) is a crucial process for visualisation and examination of abnormal tissues, especially during clinical analysis. Complexity and variations of the tumour structure magnify the challenges in the automated detection of a brain tumour in MRIs. This study presents an automatic lesion recognition method in the MRI followed by classification. In the proposed multistage image segmentation method, the intent region initialisation is performed using low‐level information by the keypoint descriptors. A set of the linear filter is used to transform low‐level information into higher‐level image features. The set of features and filter training data are accomplished to track the tumour region. The authors adopt a possibilistic model for region growing, and disparity map for the refinement process to grave consist boundary. Further, the features are extracted using the Fisher vector and autoencoder. A set of handcrafted features is also extracted using a segmentation‐based localised region to train and test the support vector machine and multilayer perceptron classifiers. The experiments that are performed using five MRI datasets confirm the superiority of proposal as that of the state‐of‐the‐art methods. It reports 94.5 and 91.76%, average accuracy of segmentation and classification, respectively.