Skip to main content

Advertisement

Log in

A comparison of two algorithms for automated stone detection in clinical B-mode ultrasound images of the abdomen

  • Published:
Journal of Clinical Monitoring and Computing Aims and scope Submit manuscript

Abstract

Ultrasound (US) imaging is an indispensible technique for detection of abdominal stones which are a serious health hazard. Segmentation of stones from abdominal ultrasound images presents a unique challenge because these images contain strong speckle noise and attenuated artifacts. In clinical situations where a large number of stones must be identified, traditional methods such as manual identification become tedious and lack reproducibility too. The necessity of obtaining high reproducibility and the need to increase efficiency motivates the development of automated and fast procedures that segment out stones of all sizes and shapes in medical images by applying image segmentation techniques. In this paper we present and compare two fully automatic and unsupervised methods for robust stone detection in B-mode ultrasound images of the abdomen. Our approaches are based on the marker controlled watershed segmentation, along with some pre-processing and post-processing procedures that eliminate the inherent problems associated with medical ultrasound images. The first algorithm (Algorithm I) utilizes the advantage of the Speckle reducing anisotropic diffusion (SRAD) technique, along with unsharp filtering and histo- gram equalization for removal of speckle noise, and the second algorithm (Algorithm II) is based on the log decompression model which too serves as a tool for minimization of speckle. Experimental results obtained from processing a set of 50 ultrasound images ensure the robustness of both the proposed algorithms. Comparative results of both the algorithms based on efficiency and relative error in stone area have been provided.

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

Access this article

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. Tsao J, Chang L-H, Lin C-H. Ultrasonic renal-stone detection and identification for extracorporeal lithotripsy. IEEE engineering in medicine and biology 27th annual conference, pp. 6254–6257, Sept. 2005.

  2. Booth B, Patel V, Lou E, Le L, Li X. Towards medical ultrasound image segmentation with limited prior knowledge. 12th digital signal processing workshop, pp. 488–493, Sept. 2006.

  3. Xie J, Jiang Y, Tsui H-T. Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Trans Med Imaging. 2005;24(1):45–57.

    Article  PubMed  Google Scholar 

  4. Michailovich O, Tannenbaum A. Segmentation of medical ultrasound images using active contours. IEEE Int Conf Image Proc. Sept. 2007;5:513–516.

    Google Scholar 

  5. Deka B, Ghosh D. Ultrasound segmentation using watersheds and region. IEEE international conference on visual information engineering, pp. 110–115, Sept. 2006.

  6. Abolmaesumi P, Sirouspour MR. An interacting multiple model probabilistic data association filter for cavity boundary extraction from ultrasound images. IEEE Trans Med Imaging. 2004;23(6):772–84.

    Article  CAS  PubMed  Google Scholar 

  7. Dubey RB, Hanmandlu M, Gupta SK. A comparison of two methods for the segmentation of masses in the digital mammograms. Comput Med Imaging Graph J., Elsevier, 2009.

  8. Martin-Fernandez M, Alberola-Lopez C. An approach for contour detection of human kidneys from ultrasound images using Markov random fields and active contours. Med Image Anal Elsevier. 2005;9:1–23.

    Article  Google Scholar 

  9. Albert W, Kocherscheidt C, Pandit M, Pfeiffer P. Segmentation of B-scan images of gallstones based on mathematical morphology. 18th Annual international conference of the IEEE engineering in medicine and biology society, pp. 909–910, Nov. 1996.

  10. Agnihotri S, Loomba H, Gupta A, Khandelwal V. Automated segmentation of gallstones in ultrasound images. 2nd IEEE international conference on computer science and information technology, pp. 56–59, 2009.

  11. Roerdink Jos BTM, Meijster A. The watershed transform: definitions, algorithms and parallelization strategies. Fundam Informaticae. 2000;41(1–2):187–228.

    Google Scholar 

  12. Beutel J, Sonka M, Michael Fitzpatrick J. Handbook of medical imaging, volume 2: medical image processing and analysis. SPIE, 2000.

  13. Yu Y, Acton ST. Speckle reducing anisotropic diffusion. IEEE Trans Image Process. 2002;11(11):1260–70.

    Article  PubMed  Google Scholar 

  14. Jain A. Fundamentals of digital image processing, Chap. 7. Prentice-Hall, 1989.

  15. Seabra J, Sanches J. Modeling log-compressed ultrasound images for radio frequency signal recovery. 30th annual international IEEE EMBS conference, pp. 426–429, 2008.

  16. Lee JS. Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell. 1980;2:165–8.

    Article  Google Scholar 

  17. Frost VS, Stiles JA, Shanmugan KS, Holtzman JC. A model for radar images and its application to adaptive digital filtering for multiplicative noise. IEEE Trans Pattern Anal Mach Intell. 1982;4:157–66.

    Article  Google Scholar 

  18. Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell. 1990;12(7):629–39.

    Article  Google Scholar 

  19. Soille P. Morphological image analysis: principles and applications. New York: Springer; 1999.

    Google Scholar 

  20. Chevrefils C, Chériet F, Grimard G, Aubin C-E. Watershed segmentation of intervertebral disk and spinal canal from MRI images. LNCS. 2007;4633:1017–27.

    Google Scholar 

  21. Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell. 1991;13(6):583–98.

    Article  Google Scholar 

  22. Sanches JM, Marques JS. Compensation of log-compressed images for 3-D ultrasound. Ultrasound Med Biol, Elsevier. 2003;29(2):239–53.

    Article  Google Scholar 

  23. Eltoft T. Modeling the amplitude statistics of ultrasonic images. IEEE Trans Med Imaging. Feb. 2006;25(2): 229–240.

    Google Scholar 

  24. Burckhardt CB. Speckle in ultrasound B-mode scans. IEEE Trans Sonics Ultrason. Jan. 1978;SU-25(1): 1–6.

    Google Scholar 

  25. Mohana Shankar P. A general statistical model for ultrasonic backscattering from tissues. IEEE Trans Ultrason Ferroelectr Freq Control. May 2000;47(3): 727–736.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abhinav Gupta M.Tech.

Additional information

Gupta A, Gosain B, Kaushal S. A comparison of two algorithms for automated stone detection in clinical B-mode ultrasound images of the abdomen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gupta, A., Gosain, B. & Kaushal, S. A comparison of two algorithms for automated stone detection in clinical B-mode ultrasound images of the abdomen. J Clin Monit Comput 24, 341–362 (2010). https://doi.org/10.1007/s10877-010-9254-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10877-010-9254-0

Keywords

Navigation