Skip to main content

Advertisement

SpringerLink
Log in

Automatic Lung Segmentation Using Control Feedback System: Morphology and Texture Paradigm

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Interstitial Lung Disease (ILD) encompasses a wide array of diseases that share some common radiologic characteristics. When diagnosing such diseases, radiologists can be affected by heavy workload and fatigue thus decreasing diagnostic accuracy. Automatic segmentation is the first step in implementing a Computer Aided Diagnosis (CAD) that will help radiologists to improve diagnostic accuracy thereby reducing manual interpretation. Automatic segmentation proposed uses an initial thresholding and morphology based segmentation coupled with feedback that detects large deviations with a corrective segmentation. This feedback is analogous to a control system which allows detection of abnormal or severe lung disease and provides a feedback to an online segmentation improving the overall performance of the system. This feedback system encompasses a texture paradigm. In this study we studied 48 males and 48 female patients consisting of 15 normal and 81 abnormal patients. A senior radiologist chose the five levels needed for ILD diagnosis. The results of segmentation were displayed by showing the comparison of the automated and ground truth boundaries (courtesy of ImgTracer™ 1.0, AtheroPoint™ LLC, Roseville, CA, USA). The left lung’s performance of segmentation was 96.52 % for Jaccard Index and 98.21 % for Dice Similarity, 0.61 mm for Polyline Distance Metric (PDM), −1.15 % for Relative Area Error and 4.09 % Area Overlap Error. The right lung’s performance of segmentation was 97.24 % for Jaccard Index, 98.58 % for Dice Similarity, 0.61 mm for PDM, −0.03 % for Relative Area Error and 3.53 % for Area Overlap Error. The segmentation overall has an overall similarity of 98.4 %. The segmentation proposed is an accurate and fully automated system.

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
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Schwarz, M. I., Matthay, R. A., Sahn, S. A., Stanford, R. E., Marmorstein, B. L., and Scheinhorn, D. J., Interstitial lung disease in polymyositis and dermatomyositis: analysis of six cases and review of the literature. Medicine 55(1):89–104, 1976.

    Article  Google Scholar 

  2. Peroš-Golubičić, T., and Sharma, O., Clinical atlas of interstitial lung disease. Springer, London, 2006.

    Google Scholar 

  3. Sharman, P., and Wood-Baker, R., Interstitial lung disease due to fumes from heat-cutting polymer rope. Occup. Med. 63(6):451–453, 2013.

    Article  Google Scholar 

  4. O’Dwyer, D. N., Armstrong, M. E., Cooke, G., Dodd, J. D., Veale, D. J., and Donnelly, S. C., Rheumatoid Arthritis (RA) associated interstitial lung disease (ILD). Eur. J. Intern Med. 24(7):597–603, 2013.

    Article  Google Scholar 

  5. Washko, G. R., Hunninghake, G. M., Fernandez, I. E., Nishino, M., Okajima, Y., Yamashiro, T., et al., Lung volumes and emphysema in smokers with interstitial lung abnormalities. N. Engl. J. Med. 364(10):897–906, 2011.

    Article  Google Scholar 

  6. Henne, E., Anderson, J. C., Lowe, N., and Kesten, S., Comparison of human lung tissue mass measurements from ex vivo lungs and high resolution CT software analysis. BMC Pulm. Med. 12(1):18, 2012.

    Article  Google Scholar 

  7. Krupinski, E. A., Berbaum, K. S., Does reader visual fatigue impact interpretation accuracy? Proc. SPIE. Med. Imaging. 7627, 76270M-1-6, 2010.

  8. Jiang, Y., Nishikawa, R. M., Schmidt, R. A., Metz, C. E., Giger, M. L., and Doi, K., Improving breast cancer diagnosis with computer-aided diagnosis. Acad. Radiol. 6(1):22–33, 1999.

    Article  Google Scholar 

  9. Doi, K., Computer-aided diagnosis in medical imaging : Historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4):198–211, 2007. doi:10.1016/j.compmedimag.2007.02.002.

    Article  Google Scholar 

  10. Kobayashi, T., Xu, X. W., MacMahon, H., Metz, C. E., and Doi, K., Effect of a computer-aided diagnosis scheme on radiologists’ performance in detection of lung nodules on radiographs. Radiology 199(3):843–848, 1996.

    Article  Google Scholar 

  11. Nagaraj, S., Rao, G. N., and Koteswararao, K., The role of pattern recognition in computer-aided diagnosis and computer-aided detection in medical imaging: A clinical validation. Int. J. Comput. Appl. 8(5):18–22, 2010.

    Google Scholar 

  12. van Rikxoort, E. M., de Hoop, B., Viergever, M. A., Prokop, M., and van Ginneken, B., Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection. Med. Phys. 36(7):2934–2947, 2009. doi:10.1118/1.3147146.

    Article  Google Scholar 

  13. van Rikxoort, E. M., and van Ginneken, B., Automated segmentation of pulmonary structures in thoracic computed tomography scans : A review. Phys. Med. Biol. 58(17):R187, 2013. doi:10.1088/0031-9155/58/17/R187.

    Article  Google Scholar 

  14. Sluimer, I., Prokop, M., and van Ginneken, B., Toward automated segmentation of the pathological lung in CT. IEEE Trans. Med. Imaging 24(8):1025–1038, 2005. doi:10.1109/TMI.2005.851757.

    Article  Google Scholar 

  15. El-Baz, A., Suri, J. S., (Eds) Lung Imaging and Computer Aided Diagnosis: CRC Press, Boca Raton, 2011.

  16. Nandy, K., Interactive segmentation and tracking in optical microscopic images. Cytom. Part A 81(5):357–359, 2012.

    Article  MathSciNet  Google Scholar 

  17. Otsu, N., A threshold selection method from gray-level histograms. Automatica 11:23–27, 1975.

    Google Scholar 

  18. Churg, A., Thurlbeck’s Pathology of the Lung. Thieme, New York, 2005.

    Google Scholar 

  19. Li, L.-N., Ouyang, J.-H., Chen, H.-L., and Liu, D.-Y., A computer aided diagnosis system for thyroid disease using extreme learning machine. J. Med. Syst. 36(5):3327–3337, 2012. doi:10.1007/s10916-012-9825-3.

    Article  Google Scholar 

  20. Sheikhtaheri, A., Sadoughi, F., and Hashemi, D. Z., Developing and using expert systems and neural networks in medicine: A review on benefits and challenges. J. Med. Syst. 38(9):1–6, 2014. doi:10.1007/s10916-014-0110-5.

    Article  Google Scholar 

  21. Wang, Q., Zhu, W., and Wang, B., Three-Dimensional SVM with Latent Variable: Application for detection of lung lesions in CT images. J. Med. Syst. 39(1):171, 2015. doi:10.1007/s10916-014-0171-5.

    Article  Google Scholar 

  22. Hu, S., Hoffman, E. A., and Reinhardt, J. M., Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans. Med. Imaging 20(6):490–498, 2001. doi:10.1109/42.929615.

    Article  Google Scholar 

  23. Itai, Y., Kim, H., Ishikawa, S., Yamamoto, A., and Nakamura, K., A segmentation method of lung areas by using snakes. Int. J. Innov. Comput. Inf. Control 3(2):277–284, 2007.

    Google Scholar 

  24. Boykov, Y., Jolly, M. P., Interactive organ segmentation using graph cuts. In Medical Image Computing and Computer-Assisted Intervention--MICCAI2000, 276–286, 2000.

  25. Osareh, A., and Shadgar, B., A segmentation method of lung cavities using region aided geometric snakes. J. Med. Syst. 34(4):419–433, 2010.

    Article  Google Scholar 

  26. Korfiatis, P., Kalogeropoulou, C., Karahaliou, A., Kazantzi, A., Skiadopoulos, S., and Costaridou, L., Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT. Med. Phys. 35(12):5290–5302, 2008.

    Article  Google Scholar 

  27. van Rikxoort, E. M., de Hoop, B., van de Vorst, S., Prokop, M., and van Ginneken, B., Automatic segmentation of pulmonary segments from volumetric chest CT scans. IEEE Trans. Med. Imaging 28(4):621–630, 2009. doi:10.1109/TMI.2008.2008968.

    Article  Google Scholar 

  28. Kakar, M., and Olsen, D. R., Automatic segmentation and recognition of lungs and lesion from CT scans of thorax. Comput. Med. Imaging Graph. 33(1):72–82, 2009. doi:10.1016/j.compmedimag.2008.10.009.

    Article  Google Scholar 

  29. Zhou, X., Hayashi, T., Hara, T., Fujita, H., Yokoyama, R., Kiryu, T., and Hoshi, H., Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images. Comput. Med. Imaging Graph. 30(5):299–313, 2006. doi:10.1016/j.compmedimag.2006.06.002.

    Article  Google Scholar 

  30. Wang, J., Li, F., and Li, Q., Automated segmentation of lungs with severe interstitial lung disease in CT. Med. Phys. 36(10):4592–9, 2009. doi:10.1118/1.3222872.

    Article  Google Scholar 

  31. Massoptier, L., Misra, A., Sowmya, A., Automatic lung segmentation in HRCT images with diffuse parenchymal lung disease using graph-cut. 24th Inter Conf In Image and Vision Computing New Zealand, 2009. IVCNZ’09. 266–270, 2009.

  32. Abbas, Q., Khan, M. T. A., Farooq, A., and Celebi, M. E., Segmentation of lungs in HRCT scan images using particle swarm optimization. Int. J. Innov. Comput. Inf. Control 9(5):2155–2165, 2013.

    Google Scholar 

  33. Boykov, Y., and Kolmogorov, V., An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9):1124–1137, 2004. doi:10.1109/TPAMI.2004.60.

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank all the radiologists and clinicians for making this study a success. We would like to express our gratitude to Mr. Ng Chuen Rue for helping to edit this manuscript. We are grateful to AtheroPoint™ LLC, Roseville, CA, USA for gracefully letting us use ImgTracer™ 1.0 software for tracing the manual borders of the lung. This study was partly funded by Universiti Teknologi Malaysia research fund (06H35).

Author information

Authors and Affiliations

  1. Department of Engineering, UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Norliza M. Noor

  2. UTM Razak School of Engineering and Advanced Technology, UniversitiTeknologi Malaysia, Kuala Lumpur, Malaysia

    Joel C. M. Than

  3. Institute of Mathematical Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia

    Omar M. Rijal

  4. Department of Diagnostic Imaging, Kuala Lumpur Hospital, Kuala Lumpur, Malaysia

    Rosminah M. Kassim

  5. Institute of Respiratory Medicine, Kuala Lumpur, Malaysia

    Ashari Yunus

  6. UC Davis School of Medicine, Sacramento, CA, USA

    Amir A. Zeki

  7. Department of Radiological Sciences, Policlinico Umberto I, University of Rome, Rome, Italy

    Michele Anzidei

  8. Azienda Ospedaliero Universitaria (A.O.U.) di Cagliari – Polo di Monserrato; Università di Cagliari, s.s. 554, Monserrato, Cagliari, 09045, Italy

    Luca Saba

  9. Global Biomedical Technologies, Roseville, CA, USA

    Jasjit S. Suri

  10. AtheroPoint™ LLC, Roseville, CA, USA

    Jasjit S. Suri

  11. Electrical Engineering Department (Affl.), Idaho State University, Pocatello, ID, USA

    Jasjit S. Suri

Authors
  1. Norliza M. Noor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joel C. M. Than
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Omar M. Rijal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rosminah M. Kassim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ashari Yunus
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amir A. Zeki
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Michele Anzidei
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Luca Saba
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jasjit S. Suri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Norliza M. Noor.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Noor, N.M., Than, J.C.M., Rijal, O.M. et al. Automatic Lung Segmentation Using Control Feedback System: Morphology and Texture Paradigm. J Med Syst 39, 22 (2015). https://doi.org/10.1007/s10916-015-0214-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-015-0214-6

Keyword

Search

Navigation