Image classification-based brain tumour tissue segmentation

Al-qazzaz, Salma, Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766, Yang, Hong ORCID: https://orcid.org/0000-0002-8429-7598, Yang, Yingxia ORCID: https://orcid.org/0000-0002-8429-7598, Xu, Ronghua, Nokes, Len ORCID: https://orcid.org/0000-0002-9504-8028 and Yang, Xin ORCID: https://orcid.org/0000-0002-8429-7598 2021. Image classification-based brain tumour tissue segmentation. Multimedia Tools and Applications 80 , pp. 993-1008. 10.1007/s11042-020-09661-4

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Abstract

Brain tumour tissue segmentation is essential for clinical decision making. While manual segmentation is time consuming, tedious, and subjective, it is very challenging to develop automatic segmentation methods. Deep learning with convolutional neural network (CNN) architecture has consistently outperformed previous methods on such challenging tasks. However, the local dependencies of pixel classes cannot be fully reflected in the CNN models. In contrast, hand-crafted features such as histogram-based texture features provide robust feature descriptors of local pixel dependencies. In this paper, a classification-based method for automatic brain tumour tissue segmentation is proposed using combined CNN-based and hand-crafted features. The CIFAR network is modified to extract CNN-based features, and histogram-based texture features are fused to compensate the limitation in the CIFAR network. These features together with the pixel intensities of the original MRI images are sent to a decision tree for classifying the MRI image voxels into different types of tumour tissues. The method is evaluated on the BraTS 2017 dataset. Experiments show that the proposed method produces promising segmentation results.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Engineering
Publisher:	Springer
ISSN:	1380-7501
Date of First Compliant Deposit:	10 September 2020
Date of Acceptance:	18 August 2020
Last Modified:	04 May 2023 17:29
URI:	https://orca.cardiff.ac.uk/id/eprint/134779

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