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Brain Tumor Segmentation from Multi-Spectral MR Image Data Using Random Forest Classifier

Csaholczi, Szabolcs and Iclanzan, David and Kovacs, Levente and Szilágyi, László (2020) Brain Tumor Segmentation from Multi-Spectral MR Image Data Using Random Forest Classifier. In: International Conference on Neural Information Processing - ICONIP2020, 18-22 Nov 2020, Bangkok, Thailand. (In Press)

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Abstract

The development of brain tumor segmentation techniques based on multi-spectral MR image data has relevant impact on the clinical practice via better diagnosis, radiotherapy planning and follow-up studies. This task is also very challenging due to the great variety of tumor appearances, the presence of several noise effects, and the differences in scanner sensitivity. This paper proposes an automatic procedure trained to distinguish gliomas from normal brain tissues in multi-spectral MRI data. The procedure is based on a random forest (RF) classifier, which uses 80 computed features beside the four observed ones, including morphological ones, gradients, and Gabor wavelet features. The intermediary segmentation outcome provided by the RF is fed to a twofold post-processing, which regularizes the shape of detected tumors and enhances the segmentation accuracy. The performance of the procedure was evaluated using the 274 records of the BraTS 2015 train data set. The achieved overall Dice scores between 85-86% represent highly accurate segmentation.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: magnetic resonance imaging, brain tumor detection, tumor segmentation, random forest
Subjects: Q Science / természettudomány > QA Mathematics / matematika > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Q Science / természettudomány > QA Mathematics / matematika > QA76 Computer software / programozás
Depositing User: Dr. László Szilágyi
Date Deposited: 24 Sep 2020 06:19
Last Modified: 24 Sep 2020 06:19
URI: http://real.mtak.hu/id/eprint/114348

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