GDL

Journal from gdlhub / 2021-08-24 11:54:25
Oleh : Angulakshmi Maruthamuthu, Lakshmi Priya Gnanapandithan G., King Saud University
Dibuat : 2021-08-14, dengan 0 file

Keyword : Brain tumour, Region of Interest, Segmentation, Superpixels, Spectral clustering, MRI
Url : http://www.sciencedirect.com/science/article/pii/S1319157817303476
Sumber pengambilan dokumen : Web

The automated brain tumour segmentation method is becoming challenging in the field of medical research as a brain tumour emerges with diverse size, shape and intensity. In this paper, spectral clustering is used for segmentation of brain tumour tissues from Magnetic resonance images (MRI) as it creates high-quality clusters. Spectral clustering suffers from dense similarity matrix construction for massive data. To overwhelm the drawback of spectral clustering, the proposed method performs the brain tumour segmentation by (i) identifying the tumorous region labelled as Region of Interest (ROI) using superpixel based spectral clustering. (ii) brain tumour tissues are then segmented by performing spectral clustering over the obtained ROI of MRI. The identification of ROI alleviates the computational burden of spectral clustering. The segmentation of ROI using spectral clustering produces high-quality clustering results for brain tumour segmentation. The observational results are taken out on BRATS 2012 dataset and evaluated using dice score, sensitivity and specificity metrics. The proposed method outperforms the other clustering methods with competitive dice score values for segmentation of edema and Tumor Core (TC) regions from MRI images.

Deskripsi Alternatif :

The automated brain tumour segmentation method is becoming challenging in the field of medical research as a brain tumour emerges with diverse size, shape and intensity. In this paper, spectral clustering is used for segmentation of brain tumour tissues from Magnetic resonance images (MRI) as it creates high-quality clusters. Spectral clustering suffers from dense similarity matrix construction for massive data. To overwhelm the drawback of spectral clustering, the proposed method performs the brain tumour segmentation by (i) identifying the tumorous region labelled as Region of Interest (ROI) using superpixel based spectral clustering. (ii) brain tumour tissues are then segmented by performing spectral clustering over the obtained ROI of MRI. The identification of ROI alleviates the computational burden of spectral clustering. The segmentation of ROI using spectral clustering produces high-quality clustering results for brain tumour segmentation. The observational results are taken out on BRATS 2012 dataset and evaluated using dice score, sensitivity and specificity metrics. The proposed method outperforms the other clustering methods with competitive dice score values for segmentation of edema and Tumor Core (TC) regions from MRI images.

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