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Proceedings Paper

Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI
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Paper Abstract

In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. To model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.

Paper Details

Date Published: 3 March 2017
PDF: 9 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342L (3 March 2017); doi: 10.1117/12.2254034
Show Author Affiliations
Linmin Pei, Old Dominion Univ. (United States)
Syed M. S. Reza, Old Dominion Univ. (United States)
Wei Li, Old Dominion Univ. (United States)
Christos Davatzikos, Univ. of Pennsylvania (United States)
Khan M. Iftekharuddin, Old Dominion Univ. (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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