Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Bauer, Stefan; Nolte, Lutz-Peter; Reyes, Mauricio (2011). Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization. In: Fichtinger, Gabor; Martel, Anne; Peters, Terry (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. 14th International Conferenc. Lecture Notes in Computer Science: Vol. 6893 (pp. 354-361). Berlin: Springer 10.1007/978-3-642-23626-6_44

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Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB

UniBE Contributor:

Bauer, Stefan; Nolte, Lutz-Peter and Reyes, Mauricio

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

0302-9743

ISBN:

978-3-642-23626-6

Series:

Lecture Notes in Computer Science

Publisher:

Springer

Language:

English

Submitter:

Mauricio Antonio Reyes Aguirre

Date Deposited:

04 Oct 2013 14:16

Last Modified:

08 Jun 2016 10:22

Publisher DOI:

10.1007/978-3-642-23626-6_44

Web of Science ID:

000306990200044

BORIS DOI:

10.7892/boris.4646

URI:

https://boris.unibe.ch/id/eprint/4646 (FactScience: 209191)

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