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Journal of Medical Imaging

Generalized method for partial volume estimation and tissue segmentation in cerebral magnetic resonance images
Author(s): April Khademi; Anastasios Venetsanopoulos; Alan R. Moody
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Paper Abstract

An artifact found in magnetic resonance images (MRI) called partial volume averaging (PVA) has received much attention since accurate segmentation of cerebral anatomy and pathology is impeded by this artifact. Traditional neurological segmentation techniques rely on Gaussian mixture models to handle noise and PVA, or high-dimensional feature sets that exploit redundancy in multispectral datasets. Unfortunately, model-based techniques may not be optimal for images with non-Gaussian noise distributions and/or pathology, and multispectral techniques model probabilities instead of the partial volume (PV) fraction. For robust segmentation, a PV fraction estimation approach is developed for cerebral MRI that does not depend on predetermined intensity distribution models or multispectral scans. Instead, the PV fraction is estimated directly from each image using an adaptively defined global edge map constructed by exploiting a relationship between edge content and PVA. The final PVA map is used to segment anatomy and pathology with subvoxel accuracy. Validation on simulated and real, pathology-free T1 MRI (Gaussian noise), as well as pathological fluid attenuation inversion recovery MRI (non-Gaussian noise), demonstrate that the PV fraction is accurately estimated and the resultant segmentation is robust. Comparison to model-based methods further highlight the benefits of the current approach.

Paper Details

Date Published: 23 April 2014
PDF: 18 pages
J. Med. Img. 1(1) 014002 doi: 10.1117/1.JMI.1.1.014002
Published in: Journal of Medical Imaging Volume 1, Issue 1
Show Author Affiliations
April Khademi, Univ. of Guelph (Canada)
Anastasios Venetsanopoulos, Univ. of Toronto (Canada)
Ryerson Univ. (Canada)
Alan R. Moody, Univ. of Toronto (Canada)
Sunnybrook Research Institute (Canada)

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