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
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.

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Categories: Images, Segmentation
Keywords: Segmentation, Expectation- Maximization, Brain, Atlas
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