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CVBIA
2005
Springer

Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines

8 years 11 months ago
Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines
Abstract. Markov Random Fields (MRFs) are a popular and wellmotivated 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.
Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bi
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where CVBIA
Authors Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bistritz, Jörg Sander, Russell Greiner
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