Skip to main content
SpringerLink
Log in

Ultrasound liver tumour active contour segmentation with initialization using adaptive Otsu based thresholding

  • Original Article
  • Published:
Research on Biomedical Engineering Aims and scope Submit manuscript

Abstract

Purpose

The advantages of ultrasound imaging are compromised by the presence of speckle, low contrast and intensity inhomogeneities especially in liver tissues, where the tumour is similar to the surrounding liver parenchyma. Accurate identification of the tumour region is vital and delineating the region of interest irrespective of the echogenicity is necessary.

Methods

In this paper, an active contour based segmentation is implemented where the contour initialization is achieved using an adaptive Otsu based thresholding. Different to existing methods, the image is neutrosophically enhanced and further despeckled using shearlets during pre-processing. The pre-processed image is thresholded with respect to predicates derived from mean, texture, gradient, phase and phase gradient. The phase gradient, which extracts the gradient information from the phase of the tumour image, is a novel concept introduced in the proposed work. The mask derived by the proposed thresholding is used as an initial contour for active contour segmentation based on local mean energies. This method of thresholding gave better mask when compared with simple Otsu thresholding or entropy thresholding.

Results

The proposed thresholding gave 5.02%, 33.91%, 34.02% and 15.65% increase and 90.7% decrease in structural similarity, Jaccard index, Dice coefficient, accuracy and mean square error respectively when compared with simple Otsu thresholding. Applying local statistics in active contours yield better results than global region based Chan-Vese segmentation. Localized energies based segmentation gave 10% more accuracy and 37.18% less error than global energy based segmentation.

Conclusion

The proposed method was applied on ultrasound liver tumour images including challenging isoechoic, slightly hypoechoic and heavily shadowed echogenecities, and showed promising segmentation results.

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

Similar content being viewed by others

References

  • Abazari R, Lakestani M. Non-subsampled Shearlet transform and log-transform methods for despeckling of medical ultrasound images. Informatica. 2019;30:1–19.

    Article  MathSciNet  Google Scholar 

  • Brattain L, Telfer BA, Dhyani M, et al. Machine learning for medical ultrasound: status, methods, and future opportunities. Abdominal Radiology. 2018;43:786–99.

    Article  Google Scholar 

  • Chan T, Vese L. Active contours without edges. IEEE Trans Image Process. 2001;10:266–77.

    Article  Google Scholar 

  • Cvancarova M, Albregtsen F, Brabrand K, Samset E. Segmentation of ultrasound images of liver tumours applying snake algorithms and GVF. International Congress Series 2005;1281:218-223. Elsevier.

  • Da Cunha AL, Zhou J, Do MN. The non-subsampled contourlet transform: theory design and applications. IEEE Trans Image Process. 2006;15:3089–101.

    Article  Google Scholar 

  • Das A, Sabut SK. Kernelized fuzzy C-means clustering with adaptive thresholding for segmenting liver tumours. Procedia Computer Science. 2016 Jan 1;92:389–95.

    Article  Google Scholar 

  • dos Anjos A, Shahbazkia HR. Bi-level image thresholding. Biosignals. 2008;2:70–6.

    Google Scholar 

  • Easley G, Labate D, Lim WQ. Sparse directional image representations using the discrete shearlet transform. Appl Comput Harmon Anal. 2008;25:25–46.

    Article  MathSciNet  Google Scholar 

  • Easley G, Labate D, Colonna F. Shearlet based total variation for denoising. IEEE Trans Image Process. 2009;18:260–8.

    Article  MathSciNet  Google Scholar 

  • Egger J, Schmalstieg D, Chen X et al. Interactive outlining of pancreatic cancer liver metastases in ultrasound images. Scientific Reports. 2017;7:892.

  • Gomez W, Leija L, Alvarenga AV, Infantosi AF, Pereira WC. Computerized lesion segmentation of breast ultrasound based on marker-controlled watershed transformation. Med Phys. 2010 Jan;37(1):82–95.

    Article  Google Scholar 

  • Haralick RM. Statistical and structural approaches to texture. Proceedings to IEEE. 1979;67:786–04.

    Article  Google Scholar 

  • Kapur JN, Sahoo PK, Wong AK. A new method for gray-level picture thresholding using the entropy of the histogram. Computer vision, graphics, and image processing. 1985 Mar 1;29(3):273–85.

    Article  Google Scholar 

  • Kuan DT, Sawchuk AA, Strand TC, Chavel P. Adaptive restoration of images with speckle. IEEE Trans Acoust Speech Signal Process. 1987;35:373–83.

    Article  Google Scholar 

  • Kumar BP, Prathap C, Dharshith CN. An automatic approach for segmentation of ultrasound liver images. International Journal of Emerging Technology and Advanced Engineering. 2013;3.

  • Lankton S, Tannenbaum A. Localizing region-based active contours. IEEE Trans Image Process. 2008;17:2029–39.

    Article  MathSciNet  Google Scholar 

  • Lee JS. Refined filtering of image noise using local statistics. Computer Graphic and Image Processing. 1981;15:380–9.

    Article  Google Scholar 

  • Liu L, Li K, Qin W, Wen T, Li L, Wu J, et al. Automated breast tumour detection and segmentation with a novel computational framework of whole ultrasound images. Medical & Biological Engineering & Computing. 2018;56:183–99.

    Article  Google Scholar 

  • Liu S, Wang Y, Yang X, Lei B, Liu L, Li SX, et al. Deep learning in medical ultrasound analysis: a review. Engineering. 2019;5:261–75.

    Article  Google Scholar 

  • Lotfollahi M, Gity M, Ye JY. Segmentation of breast ultrasound images based on active contours using neutrosophic theory. J Med Ultrason. 2018;45:205–12.

    Article  Google Scholar 

  • Madabhushi A, Yang P, Rosen M, Weinstein S. Distinguishing lesions from posterior acoustic shadowing in breast ultrasound via non-linear dimensionality reduction. International Conference of the IEEE Engineering in Medicine and Biology Society 2006;3070-3073.

  • Manikandan V, Farook M. Segmentation and classification of carotid artery ultrasound images using active contours. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2014;3:151–4.

    Google Scholar 

  • Mirzapour F, Ghassemian H. Fast GLCM and Gabor Filters for Texture Classification of Very High Resolution Remote Sensing Images. International Journal of Information & Communication Technology Research. 2015;7:21–30

  • Otsu N. A threshold selection method from gray-level histogram. IEEE Transactions on Systems. 1978;8:62–6.

    Google Scholar 

  • Poonguzhali S, Ravindran G. A complete automatic region growing method for segmentation of masses on ultrasound images. International Conference on Biomedical and Pharmaceutical Engineering. 2006:88–92.

  • Porat M, Zeevi YY. Localized texture processing in vision: analysis and synthesis in the Gaborian space. IEEE Trans Biomed Eng. 1989;2:115–29.

    Article  Google Scholar 

  • Ruikar SD, Doye DD. Wavelet based image denoising technique. Int J Adv Comput Sci Appl. 2011;2:49–53.

    Google Scholar 

  • Sajith AG, Hariharan S. Spatial fuzzy C-means clustering based liver and liver tumour segmentation on contrast enhanced CT images. International Journal of Engineering and Advanced Technology. 2015;4(3).

  • Salama AA, Smarandache F. Introduction to image processing via neutrosophic techniques. Neutrosophic Sets and Systems. 2014;5.

  • Salama AA, Smarandache F. Neutrosophic approach to grayscale images domain. Neutrosophic Sets and Systems. 2018;21.

  • Sarafis V. Phase imaging in plant cells and tissues. Biomedical Optical Phase Microscopy and Nanoscopy. 2013.

  • Shan J, Wang Y, Cheng HD. Completely automated segmentation approach for breast ultrasound images using multiple-domain features. Ultrasound Med Biol. 2012;38:262–75.

    Article  Google Scholar 

  • Sivanandan R, Jayakumari J. A novel approach to ultrasound image thresholding using phase gradients. Advances in Communication Systems and Networks 2020 (pp. 71-88). Springer, Singapore.

  • Weldon TP, Higgins WE, Dunn DF. Efficient Gabor filter design for texture segmentation. Pattern Recogn. 1996;29:2005–15.

    Article  Google Scholar 

  • Wu LU, Songde MA, Hanqing LU. An effective entropic thresholding for ultrasonic images. InProceedings. Fourteenth International Conference on Pattern Recognition (Cat. No. 98EX170) 1998;2:1552–1554.

  • Yoshida H. Segmentation of liver tumours in ultrasound images based on scale-space analysis of the continuous wavelet transform. IEEE Ultrason Symp. 1998;2:1713–6.

    Google Scholar 

  • Yu Y, Acton ST. Speckle reducing anisotropic diffusion. IEEE Trans Image Process. 2002;11.

  • Zong X, Laine AF, Geiser EA. Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing. IEEE Trans Med Imaging. 1998;17:532–40.

    Article  Google Scholar 

  • Zong J, Qiu T, Li W, Guo DM. Automatic ultrasound image segmentation based on local entropy and active contour model. Computers & Mathematics with Applications. 2019;78:929–43.

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors would like to express their thanks and gratitude for the help and suggestions provided by Dr. Vinoo Jacob, Consultant Radiologist, Cosmopolitan Hospitals Pvt. Ltd., Trivandrum, Kerala, India.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Mar Baselios College of Engineering and Technology, APJ Abdul Kalam Technological University, Trivandrum, Kerala, India

    Revathy Sivanandan & J. Jayakumari

Authors
  1. Revathy Sivanandan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Jayakumari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Revathy Sivanandan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

For this type of study, formal consent is not required.

Consent to participate

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This article does not contain patient data.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sivanandan, R., Jayakumari, J. Ultrasound liver tumour active contour segmentation with initialization using adaptive Otsu based thresholding. Res. Biomed. Eng. 37, 251–262 (2021). https://doi.org/10.1007/s42600-020-00118-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42600-020-00118-z

Keywords

Search

Navigation