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Multi-stage glioma segmentation for tumour grade classification based on multiscale fuzzy C-means

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Abstract

Segmentation of the brain glioma tumour sub-regions is critical to diagnosis and prognosis in clinical applications. This paper proposes a three-stage model to segment automatically brain tumours and their internal tissue in multi-modal brain MRI images. In the first stage, we find the whole tumour (WT) tissue and initially segment it through our neural network model in the FALIR MR sequence. The initial segmentation is fed to the active contour model to segment the precise boundary of WT in the second stage. The combination of the active contour model and the neural network increases the WT segmentation performance significantly. The segmentation of critical internal parts of the tumour, i.e., enhancing tumours (ET), is performed with a fuzzy clustering-based approach in the last stage. Specifically, we apply a multiscale fuzzy C-means (MsFCM) classification method with 12 scales to detect ET in the cropped image of the preceding stage. After segmenting the WT, ET, and normal tissue, we classify input tumours to low-grade glioma (LGG) and high-grade glioma (HGG). For this aim, a local binary pattern algorithm (LBP) and gray-level co-occurrence matrix (GLCM) matrix are used to extract features from the segmented tissues. After creating the feature vectors, we employ the neural network classifier to determine the grade of input tumours in the MR image. The main contributions of the proposed method are: 1) our fuzzy model improved the segmentation accuracy through the designed multiple scales and classes, 2) using tumour edges instead of the entire image as well as utilizing the proposed training algorithm significantly increased the speed of learning and the segmentation accuracy. The experimental results in terms of DICE score indicate that the proposed model is highly competitive compared to state-of-the-art segmentation methods.

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Availability of data and material

Our dataset is public and can be downloaded from https://www.smir.ch/BRATS/Start2013

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Funding

This work was funded in part by National Institutes of Health R01CA233888.

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Authors and Affiliations

  1. Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Mostafa Soleymanifard & Mohammad Hamghalam

  2. School of Computing, Queen’s University, Kingston, ON, Canada

    Mohammad Hamghalam

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  1. Mostafa Soleymanifard
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  2. Mohammad Hamghalam
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Correspondence to Mohammad Hamghalam.

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Soleymanifard, M., Hamghalam, M. Multi-stage glioma segmentation for tumour grade classification based on multiscale fuzzy C-means. Multimed Tools Appl 81, 8451–8470 (2022). https://doi.org/10.1007/s11042-022-12326-z

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