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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 401201, 6 pages
Research Article

Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model

1Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin 300072, China
2Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China

Received 28 March 2014; Accepted 12 May 2014; Published 29 May 2014

Academic Editor: Lei Chen

Copyright © 2014 Yu Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice’s similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.