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|Title: ||Segmentation of VOI from multidimensional dynamic PET images by integrating spatial and temporal features|
|Authors: ||Kim, Jinman|
Feng, D. David
|Subjects: ||Image segmentation|
Positron emission tomography (PET)
|Issue Date: ||Oct-2006 |
|Citation: ||IEEE transactions on information technology in biomedicine, Oct. 2006, v. 10, no. 4, p. 637-646.|
|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.|
|Description: ||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.|
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|Type: ||Journal/Magazine Article|
|Appears in Collections:||EIE Journal/Magazine Articles|
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