Title: Two-phase MRI brain tumor segmentation using random forests and level set methods
Authors: Lefkovits, László
Lefkovits, Szidónia
Citation: WSCG '2018: short communications proceedings: The 26th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech Republic May 28 - June 1 2018, p. 152-159.
Issue Date: 2018
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
URI: wscg.zcu.cz/WSCG2018/!!_CSRN-2802.pdf
ISBN: 978-80-86943-41-1
ISSN: 2464-4617
Keywords: nádor mozku;multimodalní MRI;voxel-wise segmentace;náhodný les;metoda nastavení úrovně;výběr funkce;struktura nádoru;hierarchické segmentace;učení pod dohledem
Keywords in different language: brain tumor;multimodal MRI;voxel-wise segmentation;random forest;level set method;feature selection;tumor structure;hierarchical segmentation;supervised learning
Abstract: Magnetic resonance images (MRI) in various modalities contain valuable information usable in medical diagnosis. Accurate delimitation of the brain tumor and its internal tissue structures is very important for the evaluation of disease progression, for studying the effects of a chosen treatment strategy and for surgical planning as well. At the same time early detection of brain tumors and the determination of their nature have long been desirable in preventive medicine. The goal of this study is to develop an intelligent software tool for quick detection and accurate segmentation of brain tumors from MR images. In this paper we describe the developed two-staged image segmentation framework. The first stage is a voxelwise classifier based on random forest (RF) algorithm. The second acquires the accurate boundaries by evolving active contours based on the level set method (LSM). The intelligent combination of two powerful segmentation algorithms ensures performances that cannot be achieved by either of these methods alone. In our work we used the MRI database created for the BraTS ’14-‘16 challenges, considered a gold standard in brain tumor segmentation. The segmentation results are compared with the winning state of the art methods presented at the Brain Tumor Segmentation Grand Challenge and Workshop (BratsTS).
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG '2018: Short Papers Proceedings

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