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Proceedings Paper

A novel 3D graph cut based co-segmentation of lung tumor on PET-CT images with Gaussian mixture models
Author(s): Kai Yu; Xinjian Chen; Fei Shi; Weifang Zhu; Bin Zhang; Dehui Xiang
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Paper Abstract

Positron Emission Tomography (PET) and Computed Tomography (CT) have been widely used in clinical practice for radiation therapy. Most existing methods only used one image modality, either PET or CT, which suffers from the low spatial resolution in PET or low contrast in CT. In this paper, a novel 3D graph cut method is proposed, which integrated Gaussian Mixture Models (GMMs) into the graph cut method. We also employed the random walk method as an initialization step to provide object seeds for the improvement of the graph cut based segmentation on PET and CT images. The constructed graph consists of two sub-graphs and a special link between the sub-graphs which penalize the difference segmentation between the two modalities. Finally, the segmentation problem is solved by the max-flow/min-cut method. The proposed method was tested on 20 patients’ PET-CT images, and the experimental results demonstrated the accuracy and efficiency of the proposed algorithm.

Paper Details

Date Published: 21 March 2016
PDF: 7 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842V (21 March 2016); doi: 10.1117/12.2216229
Show Author Affiliations
Kai Yu, Soochow Univ. (China)
Xinjian Chen, Soochow Univ. (China)
Fei Shi, Soochow Univ. (China)
Weifang Zhu, Soochow Univ. (China)
Bin Zhang, The First Affiliated Hospital of Soochow Univ. (China)
Dehui Xiang, Soochow Univ. (China)

Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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