Image Processing

Generalized method for partial volume estimation and tissue segmentation in cerebral magnetic resonance images

[+] Author Affiliations
April Khademi

University of Guelph, Department of Biomedical Engineering, Guelph, Ontario, N1G 2W1, Canada

Anastasios Venetsanopoulos

University of Toronto, Department of Electrical and Computer Engineering, Toronto, Ontario, M5S 3G4, Canada

Ryerson University, Department of Electrical and Computer Engineering, Toronto, Ontario, M5B 2K3, Canada

Alan R. Moody

University of Toronto, Department of Medical Imaging, Toronto, Ontario, M5T 1W7, Canada

Sunnybrook Research Institute, Department of Medical Imaging, Toronto, Ontario, M4N 3M5, Canada

J. Med. Imag. 1(1), 014002 (Apr 23, 2014). doi:10.1117/1.JMI.1.1.014002
History: Received October 4, 2013; Revised January 15, 2014; Accepted February 25, 2014
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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.

© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

April Khademi ; Anastasios Venetsanopoulos and Alan R. Moody
"Generalized method for partial volume estimation and tissue segmentation in cerebral magnetic resonance images", J. Med. Imag. 1(1), 014002 (Apr 23, 2014). ; http://dx.doi.org/10.1117/1.JMI.1.1.014002


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