Skip to main content
SpringerLink
Log in

Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. Clustering accuracy = (164+829+119+257+540+108+282+301+935+546+126+54+553)/10094 = 0.477.

References

  1. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB conference, Santiago, Chile, pp 487–499

  2. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  3. Chen H, Xu Y, Ma Y, Ma B (2010) Neural network ensemble-based computer-aided diagnosis for differentiation of lung nodules on CT images: clinical evaluation. Acad Radiol 17(5):595–602

    Article  PubMed  Google Scholar 

  4. Goldberg DE (1989) Genetic algorithm in search, optimization and machine learning. Addison-Wesley, Boston

    Google Scholar 

  5. Gonzales E, Mabu S, Taboada K, Shimada K, Hirasawa K (2010) Efficient pruning of class association rules using statistics and genetic relation algorithm. J Control Measurement Syst Integr 3(5):336–345

    Article  Google Scholar 

  6. Kim KG, Goo JM, Kim JH, Lee HJ, Min BG, Bae KT, Im JG (2005) Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: initial experience. Radiology 237(2):657–661

    Article  PubMed  Google Scholar 

  7. Kuwahara M, Kido S, Shouno H (2009) Classification of patterns for diffuse lung diseases in thoracic ct images by adaboost algorithm. In: Proceedings of SPIE, medical imaging, computer-aided diagnosis. 7260:37–1–8

  8. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  CAS  PubMed  Google Scholar 

  9. Mabu S, Hirasawa K, Hu J (2007) A graph-based evolutionary algorithm: genetic network programming (GNP) and its extension using reinforcement learning. Evol Comput 15(3):369–398

    Article  PubMed  Google Scholar 

  10. Machine Learning Group at the University of Waikato (2015) Waikato environment for knowledge analysis, open source project for machine learning. www.cs.waikato.ac.nz/ml/weka/

  11. Miranda GHB, Felipe JC (2015) Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Comput Biol Med 64:334–346

    Article  PubMed  Google Scholar 

  12. Quinlan JR (1993) C4 5: programs for machine learning, vol 1. Morgan kaufmann, Burlington

    Google Scholar 

  13. Rawat J, Singh A, Bhadauria H, Virmani J (2015) Computer aided diagnostic system for detection of leukemia using microscopic images. Procedia Comput Sci 70:748–756. In: Proceedings of the 4th international conference on eco-friendly computing and communication systems

  14. Rui X, Hirano Y, Tachibana R, Shoji K (2013) A bag-of-features approach to classify six types of pulmonary textures on high-resolution computed tomography. IEICE Trans Inf Syst 96(4):845–855

    Google Scholar 

  15. Shimada K, Hirasawa K, Hu J (2006) Genetic network programming with acquisition mechanisms of association rules. J Adv Comput Intell Intell Inform 10(1):102–111

    Google Scholar 

  16. Wedashwara W, Mabu S, Obayashi M, Kuremoto T (2016) Combination of genetic network programming and knapsack problem to support record clustering on distributed databases. Expert Syst Appl 46:15–23

    Article  Google Scholar 

  17. Zhao W, Xu R, Hirano Y, Tachibana R, Kido S (2013) Classification of diffuse lung diseases patterns by a sparse representation based method on hrct images. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE, pp 5457–5460

Download references

Author information

Authors and Affiliations

  1. Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai 2-16-1, Ube, Yamaguchi, 755-8611, Japan

    Shingo Mabu, Masanao Obayashi & Takashi Kuremoto

  2. Graduate School of Medicine, Yamaguchi University, Tokiwadai 2-16-1, Ube, Yamaguchi, 755-8611, Japan

    Noriaki Hashimoto, Yasushi Hirano & Shoji Kido

Authors
  1. Shingo Mabu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masanao Obayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takashi Kuremoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Noriaki Hashimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yasushi Hirano
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shoji Kido
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shingo Mabu.

Ethics declarations

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mabu, S., Obayashi, M., Kuremoto, T. et al. Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns. Int J CARS 12, 519–528 (2017). https://doi.org/10.1007/s11548-016-1476-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-016-1476-2

Keywords

Search

Navigation