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Discrimination of Solitary Pulmonary Nodules on CT Images Based on a Novel Automatic Weighted FCM

  • Zhang Xin
  • Jiaxing Li
  • Wang Bing
  • Ming Jun
  • Yang Ying
  • Zhang Jinxing
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)

Abstract

A novel automatic feature assessment and weighting Fuzzy C-Means (FCM) algorithm was proposed for the classification of solitary pulmonary nodules (SPN). Six pulmonary nodule features were extracted from computed tomography (CT) images, which were normalized and combined into feature sequence. The feature assessment method was used to calculate discriminative criterion of categories, where the sensitive features were selected and weighted to discriminate between benign and suspicious malignant pulmonary nodules. Forty CT slices of twenty three patients are selected to evaluate the proposed method. The experimental results show that the accuracy of discrimination is 86.3 %, the sensitiveness is 87.5 %, and the specificity is 80 %, which illustrate that the method is feasible, and have good accuracy and sensitivity.

Keywords

Classification CAD Automatic weighted preference FCM 

Notes

Acknowledgments

This research work is partially supported by the Hebei province science and technology pillar program (12275528D), the Chinese NSFC research fund (61190120, 61190124 and 61271318), the Hebei University BM Research fund (BM201110) and Technological Innovation of Undergraduate research fund (201210075008).

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Zhang Xin
    • 1
  • Jiaxing Li
  • Wang Bing
    • 2
  • Ming Jun
    • 2
  • Yang Ying
  • Zhang Jinxing
  1. 1.College of Electronic Information EngineeringHebei UniversityBaodingChina
  2. 2.College of Mathematics and Computer ScienceHebei UniversityBaodingChina

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