Case classification of pulmonary emphysema using shape and distribution of lesions

Shotaro Yoshie, Toshiyuki Tanaka, Toru Shirahata, Hiroaki Sugiura

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Pulmonary emphysema is a kind of lung disease and doctors diagnose it referring to the lung CT images findings in. Therefore computer aided diagnosis by image processing is very useful from the quantitative and objective points of view. In this study, we focused on shape and distribution of the area of lesions in the lung CT images, and propose a method for the classification of three kinds of pulmonary emphysema. We calculated five features to classify the emphysema types. Finally, we will classify emphysema by using those features and neural network in the feature work.

Original languageEnglish
Title of host publicationProceedings of SICE Annual Conference 2010, SICE 2010 - Final Program and Papers
PublisherSociety of Instrument and Control Engineers (SICE)
Number of pages4
ISBN (Print)9784907764364
Publication statusPublished - 2010 Jan 1

Publication series

NameProceedings of the SICE Annual Conference


  • CT image
  • Case classification
  • Low attenuation area
  • Neural network

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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