Skip to main content
SpringerLink
Log in

Multi-level classification of emphysema in HRCT lung images

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Emphysema is a common chronic respiratory disorder characterised by the destruction of lung tissue. It is a progressive disease where the early stages are characterised by a diffuse appearance of small air spaces, and later stages exhibit large air spaces called bullae. A bullous region is a sharply demarcated region of emphysema. In this paper, it is shown that an automated texture-based system based on co-training is capable of achieving multiple levels of emphysema extraction in high-resolution computed tomography (HRCT) images. Co-training is a semi-supervised technique used to improve classifiers that are trained with very few labelled examples using a large pool of unseen examples over two disjoint feature sets called views. It is also shown that examples labelled by experts can be incorporated within the system in an incremental manner. The results are also compared against “density mask”, currently a standard approach used for emphysema detection in medical image analysis and other computerized techniques used for classification of emphysema in the literature. The new system can classify diffuse regions of emphysema starting from a bullous setting. The classifiers built at different iterations also appear to show an interesting correlation with different levels of emphysema, which deserves more exploration.

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

Similar content being viewed by others

Notes

  1. The heuristic based filter was joint work with Mario Bou-Haidar. Refer to [23] for more details.

References

  1. Kinsella M, Müller NL, Abboud RT, Morrison NJ, DyBuncio A (1990) Quantification of emphysema by computed tomography using a “density mask” program and correlation with pulmonary function tests. Chest 97:315–321

    Article  Google Scholar 

  2. Friman O, Borga M, Lundberg M, Tylén U, Knutsson H (2002) Recognizing emphysema—a neural network approach. In: Proceedings of sixteenth international conference on pattern recognition, Quebec, Canada, pp 1–4

  3. Heitmann KR, Kauczor H, Mildenberger P, Uthmann T, Perl J, Thelen M (1997) Automatic detection of ground glass opacities on lung HRCT using multiple neural networks. Eur Radiol 9:1463–1472

    Article  Google Scholar 

  4. Mir H, Hanmandlu M, Tandon SN (1995) Texture analysis of CT images. IEEE Eng Med Biol Mag 14:781–786

    Article  Google Scholar 

  5. Muslea I, Minton S, Knoblock CA (2002) Adaptive view validation: a first step towards automatic view detection. In: Proceedings of the nineteenth international conference on machine learning, Sydney, Australia, pp 443–450

  6. Blum, Mitchell T (1998) Combining labelled and unlabelled data with co-training. In: Proceedings of the 1998 conference on computational learning theory, New York, pp 92–100

  7. Prasad M, Sowmya A (2004) Multi-level emphysema diagnosis in HRCT lung images through robust multi-view and meta-learning. In: Asian Conference on Computer Vision, Jeju, S. Korea, pp 937–942

  8. Prasad M, Sowmya A (2004) Multi-level emphysema diagnosis of HRCT lung images in an incremental framework. In: Proc SPIE Med Imaging, San Diego, USA, vol 5370, pp 42–50

  9. Raskutti B, Ferra H, Kowalczyk A (2002) Combining clustering and co-training to enhance text classification using unlabelled data. In: Proceedings of the eighth international conference on knowledge discovery and data mining, Canada, pp 620–625

  10. Zelikovitz S, Hirsh H (2001) Using LSI for text classification in the presence of background text. In: Proceedings of the tenth international conference on information and knowledge management. Atlanta, GA, pp 113–118

  11. Muslea I, Minton S, Knoblock CA (2002) Active + semi-supervised learning = robust multi-view learning. In: Proceedings of the nineteenth international conference on machine learning, Sydney, Australia, pp 435–442

  12. Kushmerick N, Johnston E, McGuinness S (2001) Information extraction by text classification. IJCAI-01 Workshop on Adaptive Text Extraction and Mining, Seattle, Washington

  13. Nahm U, Mooney R (2000) A mutually beneficial integration of data mining and information extraction. In: Proc Seventeenth Nat Conf Artif Intell, Austin, TX, pp 627–632

  14. Chiu PT, Sowmya A (2001) Lung boundary detection and low level feature extraction and analysis from HRCT images. VISIM: Information Retrieval and Exploration of Large Medical Image Collections, Utrecht, Netherlands

  15. Muslea I (2002) Active learning with multiple views. Dissertation

  16. Baum EB, Lang KE (1991) Constructing hidden units using examples and queries. In: Lippmann RP et al. (eds) Advances in neural information processing systems. Morgan Kaufmann, San Mateo, pp 904–910

    Google Scholar 

  17. Witten IH, Frank E (1999) Data mining: practical machine learning tools and techniques with Java implementations. Morgan Laufmann, San Fransisco

    Google Scholar 

  18. Milton JS, Arnold JC (1995) Introduction to probability and statistics, principles and applications for engineering and the computing sciences, 3rd edn, McGraw-Hill International, New York

  19. Mitchell T (1997) Machine learning. McGraw Hill, New York

    MATH  Google Scholar 

  20. Quinlan JR (1986) Induction of decision trees. Mach learn 1:81–106

    Google Scholar 

  21. Nigam K, McCallum A, Thrun S, Mitchell T (2000) Text classification from labelled and unlabelled documents using EM. Mach Learn 39:103–134

    Article  MATH  Google Scholar 

  22. Bowyer K, Phillips P (1998) Overview of work in empirical evaluation of computer vision algorithms. In: Bowyer K et al. (eds) Empirical evaluation techniques in computer vision, IEEE Computer Society Press, CA, pp 1–11

  23. Bou-Haidar M, Prasad M, Sowmya A (2004) Emphysema detection using a density mask. UNSW Computer Science and Engineering Technical Report no. UNSW-CSE-TR-0421

  24. Uppaluri R, Hoffman E, Sonka M, Hartley P Hunninghake G, McLennan G (1999) Computer recognition of regional lung disease patterns. Am J Respir Crit Care Med 160:648–654

    Google Scholar 

  25. Prasad M, Sowmya A (2004) Multi-class unsupervised classification with label correction of HRCT lung images. In: Proceedings of international conference on intelligent sensing and information processing, Chennai, India, pp 51–55

  26. Prasad M, Sowmya A, Koch I (2004) Feature subset selection using ICA for classifying emphysema in HRCT images. In: Proceedings of international conference on intelligent sensors, sensor networks and information processing, Melbourne, Australia, pp 515–519

  27. Bertelli L, Cucchiara R, Paternostro G, Prati A (2006) A semi-automatic system for segmentation of cardiac M-mode images. Pattern Anal Appl 9:293–306

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This research was partially supported by the Australian Research Council through a Linkage grant (2002–2004), with Medical Imaging Australasia as clinical and Philips Medical Systems as industrial partners.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, 2052, Australia

    Mithun Prasad & Arcot Sowmya

  2. I-med Network, Sydney, NSW, 2000, Australia

    Peter Wilson

Authors
  1. Mithun Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arcot Sowmya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mithun Prasad.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Prasad, M., Sowmya, A. & Wilson, P. Multi-level classification of emphysema in HRCT lung images. Pattern Anal Applic 12, 9–20 (2009). https://doi.org/10.1007/s10044-007-0093-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-007-0093-7

Keywords

Search

Navigation