Document Detail


Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information.
MedLine Citation:
PMID:  21719257     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images.
Authors:
Yong Xia; Stefan Eberl; Lingfeng Wen; Michael Fulham; David Dagan Feng
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-6-28
Journal Detail:
Title:  Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society     Volume:  -     ISSN:  1879-0771     ISO Abbreviation:  -     Publication Date:  2011 Jun 
Date Detail:
Created Date:  2011-7-1     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8806104     Medline TA:  Comput Med Imaging Graph     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2011 Elsevier Ltd. All rights reserved.
Affiliation:
Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, Australia; Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital (RPAH), Sydney, Australia; Center for Multimedia Signal Processing (CMSP), Department of Electronic & Information Engineering, Hong Kong Polytechnic University, Hong Kong.
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