EM Segmentation: Automatic Tissue Class Intensity Initialization Using K-means

Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3300
ABSTRACT
Brain tissue segmentation is important in many medical image applications. We augmented the Expectation-Maximization segmentation algorithm in Slicer3 (www.spl.harvard.edu) . Currently, in the EM Segmenter module in Slicer3 user input is necessary to set tissue-class (Gray Matter, White Matter etc.) intensity values. Our contribution to the current pipeline is to automatically compute such values using k-means clustering. Our method can be applied to scans of varying intensity profiles and thus we obviate the need for a normalization step. We applied this pipeline on multiple datasets and our method was able to accurately classify tissue-classes. The implementation was done as a standalone utility in the Python programming language (www.python.org) and work is underway to incorporate it in the EM processing pipeline.

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: Images, Segmentation
Keywords: Segmentation, Expectation- Maximization, Brain, Atlas
Export citation:

Share
backyellow
Share

Linked Publications more
backyellow
Importing Contours from DICOM-RT Structure Sets Importing Contours from DICOM-RT Structure Sets
by Dowling J., Malaterre M., Greer P.B., Salvado O.
STL file format MeshIO class for ITK STL file format MeshIO class for ITK
by Ibanez L.

View license
Loading license...

Send a message to the author
main_flat
Powered by Midas