Computer aided diagnosis system for lung cancer based on helical CT images

  • Y. Kawata
  • K. Kanazawa
  • S. Toshioka
  • N. Niki'
  • H. Satoh
  • H. Ohmatsu
  • K. Eguchi
  • N. Moriyama
Poster Session D: Biomedical Applications, Detection, Control & Surveillance, Inspection, Optical Character Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)

Abstract

Lung cancer is known as one of the most difficult cancers to cure. In order to improve the recovery rate for lung cancer, detection and treatment at an early stage of growth is necessary. For the purpose, chest CT images obtained by helical CT scanner have drawn interest in the detection of suspicious regions. This paper present a method to improve the detection accuracy of the system that we have developed to detect candidates of lung cancer based on helical CT images. The basic improvement of the system is to enhance the difference of intensity surfaces between suspicious and normal regions by using surface curvatures such as the Gaussian and the mean curvatures. Experiments to show its feasibility of improving the detection accuracy are demonstrated by applying the method to the chest CT images.

Keywords

Detection Accuracy False Negative Case Lung Cancer Screening Lung Area Intensity Surface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Y. Kawata
    • 1
  • K. Kanazawa
    • 1
  • S. Toshioka
    • 1
  • N. Niki'
    • 1
  • H. Satoh
    • 2
  • H. Ohmatsu
    • 3
  • K. Eguchi
    • 4
  • N. Moriyama
    • 3
  1. 1.Dept. of Optical. ScienceUniv. of TokushimaJapan
  2. 2.Medical Engineering LaboratoryToshiba CorporationJapan
  3. 3.National Cancer Center Hospital EastJapan
  4. 4.National Cancer Center HospitalJapan

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