Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Fuzzy entropy based optimization of clusters for the segmentation of lungs in CT scanned images

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

In this paper, we have proposed a method for segmentation of lungs from Computed Tomography (CT)-scanned images using spatial Fuzzy C-Mean and morphological techniques known as Fuzzy Entropy and Morphology based Segmentation. To determine dynamic and adaptive optimal threshold, we have incorporated Fuzzy Entropy. We have proposed a novel histogram-based background removal operator. The proposed system is capable to perform fully automatic segmentation of CT Scan Lung images, based solely on information contained by the image itself. We have used different cluster validity functions to find out optimal number of clusters. The proposed system can be used as a basic building block for Computer-Aided Diagnosis. The technique was tested against the 25 datasets of different patients received from Aga Khan Medical University, Pakistan. The results confirm the validity of technique as well as enhanced performance.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Cilva AC, Cezar P, Gattas M (2004) Diagnosis of Lung Nodule using Gini Coefficient and skeletoniz in computerized Tomography images. In: ACM symposium on applied computing, Nicosia, Cyprus, pp 243–248

  2. Dhawan AP (2003) Medical image analysis IEEE press series in biomedical engineering. Wiley, London

    Google Scholar 

  3. El-Baz A, Farag AA, Falk R, La Rocca R (2002) Detection, visualization and identification of lung abnormalities in chest spiral CT scan: Phase-I. In: International conference on biomedical engineering, Cairo, Egypt

  4. El-Baz A, Farag AA, Falk R, La Rocca R (2003) A unified approach for detection, visualization and identification of lung abnormalities in chest spiral CT scan. In: Proceedings of computer assisted radiology and surgery, London

  5. Zhao B, Gamsu G, Ginsberg MS (2003) Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm. J Appl Clin Med Phys 4(3)

  6. Cesario E, Folino F, Locane A, Manco G, Ortale R (2008) Boosting text segmentation via progressive classification. Knowl Inf Syst 15: 285–320

    Article  Google Scholar 

  7. Hoffman EA, McLennan G (1997) Assessment of the pulmonary structure-function relationship and clinical outcomes measures Quantitative volumetric CT of the lung. Acad Radiol 4(11): 758–776

    Article  Google Scholar 

  8. http://www.aku.edu/

  9. http://www.mathworks.com

  10. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York

    MATH  Google Scholar 

  11. Dehmeshki J, Ye X, Valdivieso M (2007) Automated detection of lung nodules in CT images using shape-based genetic algorithm. Comput Med Imaging Graph 31(6): 408–417

    Article  Google Scholar 

  12. Chuang K, Tzeng H, Chen S, Wu J, Chen T (2006) Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1): 9–15

    Article  Google Scholar 

  13. Rebelo MS, Furuie SS, Gutierrez MA, Costa ET, Moura LA (2007) Multiscale representation for automatic identification of structures in medical images. Comput Biol Med 37(8): 1183–1193

    Article  Google Scholar 

  14. Antonelli M, Lazzerini B, Marcelloni F (2005) Segmentation and reconstruction of the lung volume in CT images. In: 20th annual ACM symposium on applied computing, vol I. Santa Fe, New Mexico, pp 255–259, 13–17 March

  15. Memon NA, Mirza AM, Gilani SAM (2006) Deficiencies of Lung segmentation techniques using CT scan images for CAD. In: Proceedings of world academy of science, engineering and technology, vol 14

  16. Memon NA, Mirza AM, Gilani SAM (2006) Segmentation of lungs from CT scan imges for early diagnosis of lung cancer. In: Proceedings of world academy of science, engineering and technology, vol 14

  17. Haiminen N, Gionis A, Laasonen K (2008) Algorithms for unimodal segmentation with applications to unimodality detection. Knowl Inf Syst 14: 39–57

    Article  Google Scholar 

  18. Gwadera R, Gionis A, Mannila H (2008) Optimal segmentation using tree models. Knowl Inf Syst 15: 259–283

    Article  Google Scholar 

  19. Smith SM, Brady JM SUSAN (1997) A new approach to low level image processing. Int J Comput Vis 23(1): 45–78

    Article  Google Scholar 

  20. Armato SG III, Giger ML, Moran CJ (1999) Computerized detection of pulmonary nodules on CT scans. RadioGraphics 19: 1303–1311

    Google Scholar 

  21. Hu S, Huffman EA, Reinhardt JM (2001) Automatic Lung Segementation for Accurate Quantitiation of Volumetric X-Ray CT images. IEEE Trans Med Imaging 20(6)

  22. Boskovitz V, Guterman H (2002) An adaptive neuro fuzzy system for automatic image segmentation and edge detection. IEEE Trans Fuzzy Syst 10(2): 247–262

    Article  Google Scholar 

  23. Xie XL, Beni GA (1991) Validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 3: 841–846

    Article  Google Scholar 

  24. Yu-qian Z, Wei-hua G, Zhen-cheng1 C, Jing-tian1 T, Ling-yun L (1997) Medical Images Edge Detection Based on Mathematical Morphology. In: Proceedings of the IEEE engineering in medicine and biology 27th annual conference Shanghai, China

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Arfan Jaffar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jaffar, M.A., Hussain, A. & Mirza, A.M. Fuzzy entropy based optimization of clusters for the segmentation of lungs in CT scanned images. Knowl Inf Syst 24, 91–111 (2010). https://doi.org/10.1007/s10115-009-0225-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-009-0225-z

Keywords