Skip to main content

Advertisement

SpringerLink
Log in

An Effective Approach of Lesion Segmentation Within the Breast Ultrasound Image Based on the Cellular Automata Principle

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

In this paper, a novel lesion segmentation within breast ultrasound (BUS) image based on the cellular automata principle is proposed. Its energy transition function is formulated based on global image information difference and local image information difference using different energy transfer strategies. First, an energy decrease strategy is used for modeling the spatial relation information of pixels. For modeling global image information difference, a seed information comparison function is developed using an energy preserve strategy. Then, a texture information comparison function is proposed for considering local image difference in different regions, which is helpful for handling blurry boundaries. Moreover, two neighborhood systems (von Neumann and Moore neighborhood systems) are integrated as the evolution environment, and a similarity-based criterion is used for suppressing noise and reducing computation complexity. The proposed method was applied to 205 clinical BUS images for studying its characteristic and functionality, and several overlapping area error metrics and statistical evaluation methods are utilized for evaluating its performance. The experimental results demonstrate that the proposed method can handle BUS images with blurry boundaries and low contrast well and can segment breast lesions accurately and effectively.

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

References

  1. Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T: Cancer statistics, 2008. Cancer J Clin 58:71–96, 2008

    Article  Google Scholar 

  2. Cheng HD, Shi X, Min R, Hu L, Cai X, Du H: Approaches for automated detection and classification of masses in mammograms. Pattern Recognition 39(4):646–668, 2006

    Article  Google Scholar 

  3. Cheng HD, Shan J, Ju W, Guo YH, Zhang L: Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recognition 43:299–317, 2010

    Article  Google Scholar 

  4. Cheng HD, Jiang XH, Sun Y, Wang JL: Color image segmentation: advances and prospects. Pattern Recognition 34(12):2259–2281, 2001

    Article  Google Scholar 

  5. Horsch K, Giger ML, Venta LA, Vyborny CJ: Computerized diagnosis of breast lesions on ultrasound. Med Phys 29(2):157–164, 2002

    Article  PubMed  Google Scholar 

  6. Drukker K, Giger ML, Horsch K, Kupinski MA, Vyborny CJ, Mendelson EB: Computerized lesion detection on breast ultrasound. Med Phy 29(7):1438–1446, 2002

    Article  Google Scholar 

  7. Madabhushi A, Metaxas DN: Combining low–high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans Med Imag 22(2):155–169, 2003

    Article  Google Scholar 

  8. Boukerroui D, Baskurt A, Noble JA, Basset O: Segmentation of ultrasound images—multiresolution 2D and 3D algorithm based on global and local statistics. Pattern Recogn Lett 24:779–790, 2003

    Article  Google Scholar 

  9. Xiao GF, Brady M, Noble JA, Zhang YY: Segmentation of ultrasound B-mode images with intensity inhomogeneity correction. IEEE Trans Med Imag 21(1):48–57, 2002

    Article  Google Scholar 

  10. Cheng HD, Hu L, Tian J, Sun L: A novel Markov random field segmentation algorithm and its application to breast ultrasound image analysis. The 6th International Conference on Computer Vision, Pattern Recognition and Image Processing, Salt Lake City, USA, p 159, 2005

  11. Chen DR, Chang RF, Wu WJ, Moon WK, Wu WL: 3-D breast ultrasound segmentation using active contour model. Ultrasound Med Biol 29(7):1017–1026, 2003

    Article  PubMed  Google Scholar 

  12. Chang RF, Wu WJ, Moon WK, Chen WM, Lee W, Chen DR: Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model. Ultrasound Med Biol 29(11):1571–1581, 2003

    Article  PubMed  Google Scholar 

  13. Chen DR, Chang RF, Kuo WJ, Chen MC, Huang YL: Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. Ultrasound Med Biol 28(10):1301–1310, 2002

    Article  PubMed  Google Scholar 

  14. Huang YL, Chen DR: Watershed segmentation for breast tumor in 2-D sonography. Ultrasound Med Biol 30:625–632, 2004

    Article  PubMed  CAS  Google Scholar 

  15. Chen CM, Chou YH, Chen CSK: Cell-competition: a new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images. Ultrasound Med Biol 31(12):1647–1664, 2005

    Article  PubMed  Google Scholar 

  16. Chang RF, Wu WJ, Moon WK, Chen DR: Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Res Treat 89(2):179–185, 2005

    Article  PubMed  Google Scholar 

  17. Liu B, Cheng HD, Huang JH, Tian JW, Liu JF, Tang XL: Automated segmentation of ultrasonic breast lesions using statistical texture classification and active contour based on probability distance. Ultrasound Med Biol 35(8):1309–1324, 2009

    Article  PubMed  Google Scholar 

  18. Liu B, Cheng HD, Huang JH, Tian JW, Tang XL, Liu JF: Probability density difference-based active contour for ultrasound image segmentation. Pattern Recognition 43:2028–2042, 2010

    Article  Google Scholar 

  19. Chen Y, Yin RM, Flynn R, Broschat S: Aggressive region growing for speckle reduction in ultrasound images. Pattern Recognition lett 24:677–691, 2003

    Article  Google Scholar 

  20. Michailovich OV, Tannenbaum A: Despeckling of medical ultrasound images. IEEE Trans Ultrason Ferroelectr Freq Control 53(1):64–78, 2006

    Article  PubMed  Google Scholar 

  21. Noble JA, Boukerroui D: Ultrasound image segmentation: a survey. IEEE Trans Med Imag 25(8):987–1010, 2006

    Article  Google Scholar 

  22. Radu V, Thomas C: Pattern generation using likelihood inference for cellular automata. IEEE Trans Imag Pro 15(7):1718–1727, 2006

    Article  Google Scholar 

  23. Wang CY, Cheng L: Feature extracting of geographical image with status transfer of cellular automata. Proceedings of the International Symposium on Intelligent Information Systems and Applications, pp 307–309, 2009

  24. Popovici A, Popovici D: Cellular automata in image processing. Fifteenth International Symposium on Mathematical Theory of Networks and Systems, pp 34–44, 2000

  25. Hernandez G, Herrmann HJ: Cellular automata for elementary image enhancement. CVGIP: Graphical Model and Image Processing 58(1):82–89, 1996

    Article  Google Scholar 

  26. Vezhnevets V, Konouchine V: “Grow cut”—interactive multi-label N-D image segmentation by cellular automata. Proceedings of Graphicon, pp 150–156, 2005

  27. Sun YF, Chen Yan, Zhang YZ: Automated seeded region growing method for document image binarization based on topographic features. Image Analysis and Recognition, pp 200–208, 2004

  28. Weszka J, Dyer C, Rosenfeld A: A comparative study of texture measures for terrain classification. IEEE Trans Syst Man Cybern 6(4):269–285, 1976

    Google Scholar 

  29. Haralick RM, Shanmugam HK, Dinstein I: Texture parameters for image classification. IEEE Trans Syst Man Cybern 3(6):610–621, 1973

    Article  Google Scholar 

  30. Gordon R, Rangayyan RM: Feature enhancement of film mammograms using fixed and adaptive neighborhoods. Appl Opt 23(4):560–564, 1984

    Article  PubMed  CAS  Google Scholar 

  31. Kevin W, Kevin WB: Generating ROC curves for artificial neural networks. IEEE Trans Med Imag 16(3):329–336, 1997

    Article  Google Scholar 

  32. Perona J, Malik J: Scale-space and edge-detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639, 1990

    Article  Google Scholar 

  33. Czerwinski RN, Jones DL, O’Brien WD: Detection of lines and boundaries in speckle images—application to medical ultrasound. IEEE Trans Med Imag 18(2):126–136, 1999

    Article  CAS  Google Scholar 

Download references

Acknowledgment

Financial support from the National Nature Science Foundation of China (NSFC) and Outstanding Academic Researchers Project of Harbin Science and Technology Bureau is greatly appreciated (grant nos. 60873142, 61100097, and 2009RFXXS211).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, No. 92, Xidazhi Street, Harbin, 150001, People’s Republic of China

    Yan Liu, H. D. Cheng, Jianhua Huang, Yingtao Zhang & Xianglong Tang

  2. College of Science, Harbin Institute of Technology, Harbin, No. 92, Xidazhi Street, Harbin, 150001, People’s Republic of China

    Yan Liu

  3. Department of Computer Science, Utah State University, Logan, UT, 84322, USA

    H. D. Cheng

Authors
  1. Yan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. D. Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianhua Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yingtao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xianglong Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to H. D. Cheng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, Y., Cheng, H.D., Huang, J. et al. An Effective Approach of Lesion Segmentation Within the Breast Ultrasound Image Based on the Cellular Automata Principle. J Digit Imaging 25, 580–590 (2012). https://doi.org/10.1007/s10278-011-9450-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-011-9450-6

Keywords

Search

Navigation