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Gaussian Intensity Model with Neighborhood Cues for Fluid-Tissue Categorization of Multi-Sequence MR Brain Images

Katyal, Ranveer, Kuse, Manohar, Paneri, Sahil
The LNM Institute of Information Technology, Jaipur, India
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Published in The MIDAS Journal - MICCAI 2013 Workshop: The MICCAI Grand Challenge on MR Brain Image Segmentation (MRBrainS13) .
Submitted by Ranveer Katyal on 2013-10-23 03:16:12.

This work presents an automatic brain MRI segmentation method which can classify brain voxels into one of three main tissue types: gray matter (GM), white matter (WM) and Cerebro-spinal Fluid (CSF). Intensity-model based classification of MR images has proven problematic. The statistical approach does not carry any spatial, textural and neighborhood information in it. We propose to use a computationally fast and novel feature-set to facilitate voxel wise classification based on regional intensity, texture, spatial location of voxels in addition to posterior probability estimates. Information available through T1-weighted (T1), T1-weighted inversion recovery (IR) and T2-weighted FLAIR (FLAIR) MRI sequences was also leveraged. An aggregate overlap of 90.21% for all intracranial structures was reported between the automatic classification and available expert annotation as measured by the DICE coefficient.