Automated lung nodule detection using positron emission tomography/computed tomography

Atsushi Teramoto, Hiroshi Fujita

研究成果: Chapter

9 被引用数 (Scopus)

抄録

Lung cancer is a leading cause of death in human globally. Owing to the low survival rates among lung cancer patients, it is essential to detect and treat cancer at an early stage. In some countries, positron emission tomography (PET)/X-ray computed tomography (CT) examination is also used for the cancer screening in addition to diagnosis and follow-up of treatment. PET/CT images provide both anatomical and functional information of the lung cancer. However, radiologists must examine a large number of these images and therefore, support tools for the localization of lung nodule are desired. This chapter highlights our recent contributions to a hybrid detection scheme of lung nodules in PET/CT images. In the CT image, a massive region is first detected using a cylindrical nodule enhancement filter (CNEF), which is a cylindrical kernel shaped by contrast enhancement filter. Subsequently, high-uptake regions detected by the PET images are merged with the region detected by the CT image. False positives (FPs) among the leading candidates are eliminated by a rule-based classifier and three support vector machines based on the characteristic features obtained from CT and PET images. Experimentally, the detection capability was evaluated using 100 cases of PET/CT images. As a result, the sensitivity in detecting candidates was 83%, with 5 FPs/case. These results indicate that the proposed hybrid method may be useful for the computer-aided detection of lung cancer in clinical practice.

本文言語English
ホスト出版物のタイトルIntelligent Systems Reference Library
出版社Springer Science and Business Media Deutschland GmbH
ページ87-110
ページ数24
DOI
出版ステータスPublished - 2018

出版物シリーズ

名前Intelligent Systems Reference Library
140
ISSN(印刷版)1868-4394
ISSN(電子版)1868-4408

All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンス(全般)
  • 情報システムおよび情報管理
  • 図書館情報学

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