Segmenting brain tumors with conditional random fields and support vector machines

Authors:
Chi-Hoon Lee;Mark Schmidt;Albert Murtha;Aalo Bistritz;Jöerg Sander;Russell Greiner
Affiliations:
Department of Computing Science, University of Alberta;Department of Computing Science, University of Alberta;Division of Radiation Oncology;Division of Diagnostic Imaging, Department of Oncology, Cross Cancer Institute, Edmonton, AB, Canada;Department of Computing Science, University of Alberta;Department of Computing Science, University of Alberta
Venue:
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Year:
2005

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Abstract

Markov Random Fields (MRFs) are a popular and well-motivated model for many medical image processing tasks such as segmentation. Discriminative Random Fields (DRFs), a discriminative alternative to the traditionally generative MRFs, allow tractable computation with less restrictive simplifying assumptions, and achieve better performance in many tasks. In this paper, we investigate the tumor segmentation performance of a recent variant of DRF models that takes advantage of the powerful Support Vector Machine (SVM) classification method. Combined with a powerful Magnetic Resonance (MR) preprocessing pipeline and a set of ‘alignment-based’ features, we evaluate the use of SVMs, MRFs, and two types of DRFs as classifiers for three segmentation tasks related to radiation therapy target planning for brain tumors, two of which do not rely on ‘contrast agent’ enhancement. Our results indicate that the SVM-based DRFs offer a significant advantage over the other approaches.