Please use this identifier to cite or link to this item:
Title: Segmentation of VOI from multidimensional dynamic PET images by integrating spatial and temporal features
Authors: Kim, J
Cai, W
Feng, DD
Eberl, S
Keywords: Image segmentation
Multivolume rendering
Positron emission tomography (PET)
Quantitative evaluation
Issue Date: Oct-2006
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on information technology in biomedicine, Oct. 2006, v. 10, no. 4, p. 637-646 How to cite?
Journal: IEEE transactions on information technology in biomedicine 
Abstract: Segmentation of multidimensional dynamic positron emission tomography (PET) images into volumes of interest (VOIs) exhibiting similar temporal behavior and spatial features is a challenging task due to inherently poor signal-to-noise ratio and spatial resolution. In this study, we propose VOI segmentation of dynamic PET images by utilizing both the three-dimensional (3-D) spatial and temporal domain information in a hybrid technique that integrates two independent segmentation techniques of cluster analysis and region growing. The proposed technique starts with a cluster analysis that partitions the image based on temporal similarities. The resulting temporal partitions, together with the 3-D spatial information are utilized in the region growing segmentation. The technique was evaluated with dynamic 2-[[sup 18]F] fluoro-2-deoxy-D-glucose PET simulations and clinical studies of the human brain and compared with the k-means and fuzzy c-means cluster analysis segmentation methods. The quantitative evaluation with simulated images demonstrated that the proposed technique can segment the dynamic PET images into VOIs of different kinetic structures and outperforms the cluster analysis approaches with notable improvements in the smoothness of the segmented VOIs with fewer disconnected or spurious segmentation clusters. In clinical studies, the hybrid technique was only superior to the other techniques in segmenting the white matter. In the gray matter segmentation, the other technique tended to perform slightly better than the hybrid technique, but the differences did not reach significance. The hybrid technique generally formed smoother VOIs with better separation of the background. Overall, the proposed technique demonstrated potential usefulness in the diagnosis and evaluation of dynamic PET neurological imaging studies.
ISSN: 1089-7771
DOI: 10.1109/TITB.2006.874192
Rights: © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Kim_et_al_Integrating_Spatial_Temporal.pdf776.95 kBAdobe PDFView/Open
View full-text via PolyU eLinks SFX Query
Show full item record
PIRA download icon_1.1View/Download Contents


Last Week
Last month
Citations as of Aug 15, 2018


Last Week
Last month
Citations as of Aug 16, 2018

Page view(s)

Last Week
Last month
Citations as of Aug 21, 2018


Citations as of Aug 21, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.