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Classification of Ground-Glass Opacity Nodules with Small Solid Components using Multiview Images and Texture Analysis in Chest CT Images

흉부 CT 영상에서 다중 뷰 영상과 텍스처 분석을 통한 고형 성분이 작은 폐 간유리음영 결절 분류

  • Lee, Seon Young (Dept. of Software Convergence, Seoul Women's University) ;
  • Jung, Julip (Dept. of Software Convergence, Seoul Women's University) ;
  • Lee, Han Sang (School of Electrical Engineering, KAIST) ;
  • Hong, Helen (Dept. of Software Convergence, Seoul Women's University)
  • Received : 2017.03.13
  • Accepted : 2017.06.21
  • Published : 2017.07.31

Abstract

Ground-glass opacity nodules(GGNs) in chest CT images are associated with lung cancer, and have a different malignant rate depending on existence of solid component in the nodules. In this paper, we propose a method to classify pure GGNs and part-solid GGNs using multiview images and texture analysis in pulmonary GGNs with solid components of 5mm or smaller. We extracted 1521 features from the GGNs segmented from the chest CT images and classified the GGNs using a SVM classification model with selected features that classify pure GGNs and part-solid GGNs through a feature selection method. Our method showed 85% accuracy using the SVM classifier with the top 10 features selected in the multiview images.

Keywords

References

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