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• W2621187744 abstract "The fuzzy degree of lung nodule boundary is the most important cue to judge the lung cancer in CT images. Based on this feature, the paper proposes a novel lung cancer detection method for CT images based on the super-pixels and the level set segmentation methods. In the proposed methods, the super-pixels method is used to segment the lung region and the suspected lung cancer lesion region in the CT image. The super-pixels method and a level set method are used to segment the suspected lung cancer lesion region simultaneously. Finally, the cancer is determined by the difference between results of the two segmentation methods. Experimental results show that the proposed algorithm has a high accuracy for lung cancer detection in CT images. For gross glass nodule, pleural nodule, the vascular nodules and solitary nodules, the sensitivity of the detection algorithm are respectively 91.3%, 96.3%, 80.9% and 82.3%." @default.
• W2621187744 created "2017-06-09" @default.
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• W2621187744 date "2017-07-20" @default.
• W2621187744 modified "2023-09-27" @default.
• W2621187744 title "A novel lung cancer detection algorithm for CADs based on SSP and Level Set" @default.
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• W2621187744 doi "https://doi.org/10.3233/thc-171338" @default.
• W2621187744 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28582923" @default.
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