A Solitary Feature-Based Lung Nodule Detection Approach for Chest X-Ray Radiographs

Xuechen Li, Linlin Shen, Suhuai Luo

Research output: Journal PublicationArticlepeer-review

24 Citations (Scopus)

Abstract

Lung cancer is one of the most deadly diseases. It has a high death rate and its incidence rate has been increasing all over the world. Lung cancer appears as a solitary nodule in chest x-ray radiograph (CXR). Therefore, lung nodule detection in CXR could have a significant impact on early detection of lung cancer. Radiologists define a lung nodule in CXR as 'solitary white nodule-like blob.' However, the solitary feature has not been employed for lung nodule detection before. In this paper, a solitary feature-based lung nodule detection method was proposed. We employed stationary wavelet transform and convergence index filter to extract the texture features and used AdaBoost to generate white nodule-likeness map. A solitary feature was defined to evaluate the isolation degree of candidates. Both the isolation degree and the white nodule likeness were used as final evaluation of lung nodule candidates. The proposed method shows better performance and robustness than those reported in previous research. More than 80% and 93% of lung nodules in the lung field in the Japanese Society of Radiological Technology (JSRT) database were detected when the false positives per image were two and five, respectively. The proposed approach has the potential of being used in clinical practice.

Original languageEnglish
Pages (from-to)516-524
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Volume22
Issue number2
DOIs
Publication statusPublished - Mar 2018
Externally publishedYes

Keywords

  • AdaBoost
  • computer-aided detection
  • lung nodule detection
  • solitary-feature
  • x-ray radiograph

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management

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