Sensitivity improvement of automatic pulmonary nodules detection in chest X-ray CT images

Noriyasu Homma, Satoshi Shimoyama, Tadashi Ishibashi, Yusuke Kawazumi, Makoto Yoshizawa

Research output: Contribution to journalArticle

Abstract

In this article, we develop an automatic detection method for non-isolated pulmonary nodules as part of a computer-aided diagnosis (CAD) system for lung cancers in chest X-ray computed tomography (CT) images. An essential core of the method is to separate non-isolated nodules from connecting structures such as the chest wall and blood vessels. The isolated nodules can be detected more easily by the CAD systems developed previously. To this end, we propose a preprocessing technique for nodule candidate detection by using double-threshold binarization. We evaluate the performance using the receiver operating characteristic (ROC) analysis in clinical chest CT images. The results suggest that the detection rate for non-isolated nodules by the proposed method is superior to that by the conventional preprocessing methods.

Original languageEnglish
Pages (from-to)526-529
Number of pages4
JournalArtificial Life and Robotics
Volume15
Issue number4
DOIs
Publication statusPublished - 2010

Keywords

  • Computer-aided diagnosis
  • Lung cancer
  • Lung nodule
  • Multiple thresholds
  • X-ray CT

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence

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