Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/29240

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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