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Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI
- Source :
- Medical Imaging: Computer-Aided Diagnosis
- Publication Year :
- 2017
- Publisher :
- SPIE, 2017.
-
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. In order 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.
- Subjects :
- Computer science
Quantitative Biology::Tissues and Organs
Physics::Medical Physics
0206 medical engineering
02 engineering and technology
Article
Quantitative Biology::Cell Behavior
030218 nuclear medicine & medical imaging
Brain cancer
03 medical and health sciences
0302 clinical medicine
Fractal
medicine
Ground truth
medicine.diagnostic_test
business.industry
Magnetic resonance imaging
Pattern recognition
Multifractal system
Image segmentation
020601 biomedical engineering
Fractal analysis
Intensity (physics)
Data set
Artificial intelligence
business
Subjects
Details
- ISSN :
- 0277786X
- Database :
- OpenAIRE
- Journal :
- SPIE Proceedings
- Accession number :
- edsair.doi.dedup.....13d4f611686740e089ce938200367651