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Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns

  • Shingo MabuEmail author
  • Masanao Obayashi
  • Takashi Kuremoto
  • Noriaki Hashimoto
  • Yasushi Hirano
  • Shoji Kido
Original Article

Abstract

Purpose

For realizing computer-aided diagnosis (CAD) of computed tomography (CT) images, many pattern recognition methods have been applied to automatic classification of normal and abnormal opacities; however, for the learning of accurate classifier, a large number of images with correct labels are necessary. It is a very time-consuming and impractical task for radiologists to give correct labels for a large number of CT images. In this paper, to solve the above problem and realize an unsupervised class labeling mechanism without using correct labels, a new clustering algorithm for diffuse lung diseases using frequent attribute patterns is proposed.

Methods

A large number of frequently appeared patterns of opacities are extracted by a data mining algorithm named genetic network programming (GNP), and the extracted patterns are automatically distributed to several clusters using genetic algorithm (GA). In this paper, lung CT images are used to make clusters of normal and diffuse lung diseases.

Results

After executing the pattern extraction by GNP, 1,148 frequent attribute patterns were extracted; then, GA was executed to make clusters. This paper deals with making clusters of normal and five kinds of abnormal opacities (i.e., six-class problem), and then, the proposed method without using correct class labels in the training showed 47.7 % clustering accuracy.

Conclusion

It is clarified that the proposed method can make clusters without using correct labels and has the potential to apply to CAD, reducing the time cost for labeling CT images.

Keywords

Computer-aided diagnosis Diffuse lung diseases Unsupervised learning Clustering Data mining Evolutionary computation 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Human and animal rights

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© CARS 2016

Authors and Affiliations

  • Shingo Mabu
    • 1
    Email author
  • Masanao Obayashi
    • 1
  • Takashi Kuremoto
    • 1
  • Noriaki Hashimoto
    • 2
  • Yasushi Hirano
    • 2
  • Shoji Kido
    • 2
  1. 1.Graduate School of Science and EngineeringYamaguchi UniversityUbe, YamaguchiJapan
  2. 2.Graduate School of MedicineYamaguchi UniversityUbe, YamaguchiJapan

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