A novel 3D graph cut based co-segmentation of lung tumor on PET-CT images with Gaussian mixture models

Kai Yu; Xinjian Chen; Fei Shi; Weifang Zhu; Bin Zhang; Dehui Xiang

doi:10.1117/12.2216229

21 March 2016 A novel 3D graph cut based co-segmentation of lung tumor on PET-CT images with Gaussian mixture models

Kai Yu, Xinjian Chen, Fei Shi, Weifang Zhu, Bin Zhang, Dehui Xiang

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97842V (2016) https://doi.org/10.1117/12.2216229
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Kai Yu, Xinjian Chen, Fei Shi, Weifang Zhu, Bin Zhang, and Dehui Xiang "A novel 3D graph cut based co-segmentation of lung tumor on PET-CT images with Gaussian mixture models", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842V (21 March 2016); https://doi.org/10.1117/12.2216229

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