MR Brain Segmentation using Decision Trees
logo

Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3441
Segmentation of the human cerebrum from magnetic resonance images (MRI) into its component tissues has been a defining problem in medical imaging. Until recently, this has been solved as the tissue classification of the T1-weighted (T1-w) MRI, with numerous solutions for this problem having appeared in the literature. The clinical demands of understanding lesions, which are indistinguishable from gray matter in T1-w images, has necessitated the incorporation of T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) images to improve segmentation of the cerebrum. Many of the existing methods fail to handle the second data channel gracefully, because of assumptions about their model. In our new approach, we explore a model free algorithm which uses a classification technique based on ensembles of decision trees to learn the mapping from an image feature to the corresponding tissue label. We use corresponding image patches from a registered set of T1-w and FLAIR images with a manual segmentation to construct our decision
tree ensembles. Our method is efficient, taking less than two minutes on a 240x240x48 volume. We conduct experiments on five training sets in a leave-one-out fashion, as well as validation on an additional twelve subjects, and a landmark validation experiment on another cohort of five
subjects.

Reviews
There is no review at this time. Be the first to review this publication!

Quick Comments


Resources
backyellow
Download All
Download Paper , View Paper

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

Information more
backyellow
Categories: Atlas-based segmentation, Decision trees and non-metric classification
Keywords: Brain MRI, Brain Segmentation, Patches, Classification, Decision Trees, Random Forests
Tracking Number: 1R21EB012765-01A1
Export citation:

Share
backyellow
Share

Linked Publications more
backyellow
Modified Expectation Maximization Method for Automatic Segmentation of MR Brain Images Modified Expectation Maximization Method for Automatic Segmentation of MR Brain Images
by R M.P., R S.S.K.
Open Source Software to Visualize Complex Data on Remote CEA's Supercomputing Facilities Open Source Software to Visualize Complex Data on Remote CEA's Supercomputing Facilities
by Vivodtzev F., Carrard T.

View license
Loading license...

Send a message to the author
main_flat
Powered by Midas