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

Srinivasan, Padmapriya, Shenton, Martha, Bouix, Sylvain
Brigham and Women's Hospital
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3300
New: Prefer using the following doi: https://doi.org/10.54294/rk0lmd
Published in The MIDAS Journal - Medical Imaging and Computing.
Submitted by Padmapriya Srinivasan on 2011-07-13 10:21:17.

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.