Automated Brain-Tissue Segmentation by Multi-Feature SVM Classification
logo

Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3443
We present a method for automated brain-tissue segmentation through voxelwise classification. Our algorithm uses manually labeled training images to train a support vector machine (SVM) classifier, which is then used for the segmentation of target images. The classification incorporates voxel intensities from a T1-weighted scan, an IR scan, and a FLAIR scan; features to encode the voxel position in the image; and Gaussian-scale-space features and Gaussian-derivative features at
multiple scales to facilitate a smooth segmentation.
An experiment on data from the MRBrainS13 brain-tissue-segmentation challenge showed that our algorithm produces reasonable segmentations in a reasonable amount of time.

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: Classification, Mathematics, Segmentation
Keywords: Machine Learning, Pattern Recognition, Classification, Segmentation, Brain, MRI
Tracking Number: NWO 639.022.010
Export citation:

Share
backyellow
Share

Linked Publications more
backyellow
The graph windowed Fourier transform: a tool to quantify the gyrification of the cerebral cortex The graph windowed Fourier transform: a tool to quantify the gyrification of the cerebral cortex
by Rabiei H., Richard F., Roth M., Anton J., Coulon O., Lefevre J.
Facet Analyser: ParaView plugin for automated facet detection and measurement of interplanar angles... Facet Analyser: ParaView plugin for automated facet detection and measurement of interplanar angles...
by Grothausmann R., Beare R.

View license
Loading license...

Send a message to the author
main_flat
Powered by Midas