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A tissue classification approach for brain tumor segmentation using MRI

Seferlis Stavros, Zarifis Georgios, Giakos George C., Pezoulas Vasileios, Zervakis Michail, Pologiorgi Ifigeneia, Tsalikis, George

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Year 2018
Type of Item Conference Full Paper
Bibliographic Citation V.C. Pezoulas, M. Zervakis, I. Pologiorgi, S. Seferlis, G.M. Tsalikis, G. Zarifis and G.C. Giakos, "A tissue classification approach for brain tumor segmentation using MRI," in 2017 IEEE International Conference on Imaging Systems and Techniques, 2018, pp. 1-6. doi: 10.1109/IST.2017.8261542
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Innovative practices in the sector of medical imaging are nowadays applied in order to upgrade the medical services provided to individuals, giving answers to crucial medical issues, something impossible in the past. Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues such as white/white matter and cerebrospinal fluid. The present article attempts to provide an application of these practices on brain tumor segmentation using MRI data. More specifically, a new skull stripping method is proposed based on the Normalized-cut (N-cut) algorithm and then a histogram classification approach is applied on the skull-free images for a more accurate brain tumor segmentation alongside with an entropy filter for highlighting the necrotic tissue.