| Title: | Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit |
| Author: | Melonakos, John ; Al-Hakim, Ramsey ; Fallon, James ; Tannenbaum, Allen R. |
| Abstract: | An Insight Toolkit (ITK) implementation of our knowledge-based segmentation algorithm applied to brain MRI scans is presented in this paper. Our algorithm is a refinement of the work of Teo, Saprio, and Wandall. The basic idea is to incorporate prior knowledge into the segmentation through Bayes’ rule. Image noise is removed via an affine invariant anisotropic smoothing of the posteriors as in Haker et. al. We present the results of this code on two different projects. First, we show the effect of applying this code to skull-removed brain MRI scans. Second, we show the effect of applying this code to the extraction of the DLPFC from a user-defined subregion of brain MRI data.We present our results on brain MRI scans, comparing the results of the knowledge-based segmentation to manual segmentations on datasets of schizophrenic patients. |
| Description: | Presented at the 2005 MICCAI Workshop on Open-Source Software, October 30th, 2005, Palm Springs, CA, USA. Hosted by The Insight Software Consortium (ISC) and The National Alliance for Medical Image Computing (NA-MIC) |
| Type: | Proceedings |
| URI: | http://hdl.handle.net/1853/29240 |
| Citation: | John Melonakos, Ramsey Al-Hakim, James Fallon and Allen Tannenbaum, "Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit," Insight Journal - 2005 MICCAI Open-Source Workshop: http://hdl.handle.net/1926/44 |
| Date: | 2005-09 |
| Contributor: |
Georgia Institute of Technology
University of California, Irvine |
| Publisher: |
Georgia Institute of Technology
Insight Software Consortium |
| Subject: |
Knowledge-based segmentation
Brain MRI scans DLPFC Bayes' rule |
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| 2005_insight_melonakos_001.pdf | 143.3Kb |
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