Journal of Northeastern University:Natural Science ›› 2017, Vol. 38 ›› Issue (5): 639-644.DOI: 10.12068/j.issn.1005-3026.2017.05.007

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A Lung Cancer Detection Algorithm Based on CV Model and Improved ME Model

PAK Chun-hyok1, CAO Peng1, 2, ZHAO Hai1, ZHU Hong-bo1   

  1. 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China; 2. School of Public Foundation, China Medical University, Shenyang 110122, China.
  • Received:2015-12-11 Revised:2015-12-11 Online:2017-05-15 Published:2017-05-11
  • Contact: CAO Peng
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Abstract:

According to solve the problem that it is difficult to automatically detect lung nodule lesions in CT images, a lung cancer detection algorithm was proposed based on Chan-Vese model (CV model) and improved mean square error model (ME model). As the degree of fuzziness of nodular boundary is the most important indicator of evaluating lung nodule in CT images study, two interactive image segmentation algorithms were employed in the proposed method based on CV model and improved ME model to process the CT image. Since the shrinkage of these two algorithms vary, two different nodular boundaries were got, and the degree of fuzziness of nodular according to the boundary difference was computed. Lastly, by comparing the degree of fuzziness of nodular, the threshold value to diagnose cancer was determined. The experimental evaluation demonstrates that compared with existing methods, the algorithm can detect lung cancer with higher accuracy in CT images.

Key words: image segmentation, level-set method, CV model, ME model, lung nodule detection

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