MS Lesion Segmentation based on Hidden Markov Chains
logo

Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1450
In this paper, we present a new automatic robust algorithm to segment multimodal brain MR images with Multiple Sclerosis (MS) lesions. The method performs tissue classification using a Hidden Markov Chain (HMC) model and detects MS lesions as outliers to the model. For this aim, we use the Trimmed Likelihood Estimator (TLE) to extract outliers. Furthermore, neighborhood information is included using the HMC model and we propose to incorporate a priori information brought by a probabilistic atlas.

Reviews
plus Hidden Markov Chain Tissue Classification and outlier detection for MS segmentation by Martin Styner on 07-29-2008 for revision #1
starstarstarstarstar expertise: 5 sensitivity: 5
plus Review by Simon Warfield on 07-25-2008 for revision #1
starstarstarstarstar expertise: 5 sensitivity: 5
Add a new review
Quick Comments


Resources
backyellow
Download All

Statistics more
backyellow
Global rating: starstarstarstarstar
Review rating: starstarstarstarstar [review]
Paper Quality: plus minus

Information more
backyellow
Categories: Atlas-based segmentation, Bayesian Decision Theory, Classification, Density Estimation, Density Functions, Missing and Noisy Features, Mixture of densities, Segmentation, Unsupervised learning and clustering
Keywords: Markov models, outlier detection, probabilistic atlas
Export citation:

Share
backyellow
Share

Linked Publications more
backyellow
Surface Meshes Incremental Decimation Framework Surface Meshes Incremental Decimation Framework
by Gelas A., Gouaillard A., Megason S.
3D Segmentation in the Clinic: A Grand  Challenge II: MS lesion segmentation 3D Segmentation in the Clinic: A Grand Challenge II: MS lesion segmentation
by Styner M., Lee J., Chin B., Chin M.S., Commowick O., Tran H., Markovic-Plese S., Jewells V., Warfield S.

View license
Loading license...

Send a message to the author
main_flat
Powered by Midas