Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29675
Title: Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information
Authors: Xia, Y
Eberl, S
Wen, L
Fulham, M
Feng, DD
Keywords: Dual-modality medical imaging
Functional PET imaging
Image segmentation
Medical image analyze
Issue Date: 2012
Publisher: Pergamon-Elsevier Science Ltd
Source: Computerized medical imaging and graphics, 2012, v. 36, no. 1, p. 47-53 How to cite?
Journal: Computerized Medical Imaging and Graphics 
Abstract: 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.
URI: http://hdl.handle.net/10397/29675
DOI: 10.1016/j.compmedimag.2011.06.004
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