ArticleComputational and mathematical methods in medicine2014
Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.
Article in Computational and mathematical methods in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Review
- AI-based detection of lung lesions in [EJNMMI physics · 2021Article
- Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.PloS one · 2021Article
- Development and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT.Journal of digital imaging · 2018Article
- Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features.International journal of biomedical imaging · 2015Article
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7 authors.
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Abstract
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.
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