Skip to main content

Using neutrosophic graph cut segmentation algorithm for qualified rendering image selection in thyroid elastography video

Health Information Science and Systems volume 5, Article number: 8 (2017) Cite this article

Abstract

Recently, elastography has become very popular in clinical investigation for thyroid cancer detection and diagnosis. In elastogram, the stress results of the thyroid are displayed using pseudo colors. Due to variation of the rendering results in different frames, it is difficult for radiologists to manually select the qualified frame image quickly and efficiently. The purpose of this study is to find the qualified rendering result in the thyroid elastogram. This paper employs an efficient thyroid ultrasound image segmentation algorithm based on neutrosophic graph cut to find the qualified rendering images. Firstly, a thyroid ultrasound image is mapped into neutrosophic set, and an indeterminacy filter is constructed to reduce the indeterminacy of the spatial and intensity information in the image. A graph is defined on the image and the weight for each pixel is represented using the value after indeterminacy filtering. The segmentation results are obtained using a maximum-flow algorithm on the graph. Then the anatomic structure is identified in thyroid ultrasound image. Finally the rendering colors on these anatomic regions are extracted and validated to find the frames which satisfy the selection criteria. To test the performance of the proposed method, a thyroid elastogram dataset is built and totally 33 cases were collected. An experienced radiologist manually evaluates the selection results of the proposed method. Experimental results demonstrate that the proposed method finds the qualified rendering frame with 100% accuracy. The proposed scheme assists the radiologists to diagnose the thyroid diseases using the qualified rendering images.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3

References

  1. Asteria C, Giovanardi A, Pizzocaro A, Cozzaglio L, Morabito A, Somalvico F, Zoppo A. US-elastography in the differential diagnosis of benign and malignant thyroid nodules. Thyroid. 2008;18(5):523–31.

    Article  PubMed  Google Scholar 

  2. Erkamp R, Wiggins P, Skovoroda A, Emelianov SY, O’donnell, M. Measuring the elastic modulus of small tissue samples. Ultrason Imaging. 1998;20(1):17–28.

    CAS  Article  PubMed  Google Scholar 

  3. Itoh A, Ueno E, Tohno E, Kamma H, Takahashi H, Shiina T, Yamakawa M, Matsumura T. Breast disease: clinical application of US elastography for diagnosis. Radiology. 2006;239(2):341–50.

    Article  PubMed  Google Scholar 

  4. Lyshchik A, Higashi T, Asato R, Tanaka S, Ito J, Mai JJ, Pellot-Barakat C, Insana MF, Brill AB, Saga T. Thyroid gland tumor diagnosis at US elastography. Radiology. 2005;237(1):202–11.

    Article  PubMed  Google Scholar 

  5. Miyanaga N, Akaza H, Yamakawa M, Oikawa T, Sekido N, Hinotsu S, Kawai K, Shimazui T, Shiina T. Tissue elasticity imaging for diagnosis of prostate cancer: a preliminary report. Int J Urol. 2006;13(12):1514–8.

    Article  PubMed  Google Scholar 

  6. Kaur J, Jindal A. Comparison of thyroid segmentation algorithms in ultrasound and scintigraphy images. Int J Comput Appl. 2012;50(23):24–7.

    Google Scholar 

  7. Mahmood NH, Rusli AH. Segmentation and area measurement for thyroid ultrasound image. Int J Sci Eng Res 2011;2(12).

  8. Selvathi D, Sharnitha V. Thyroid segmentation in ultrasound images using support vector machine. Int J Neural Netw Appl. 2011;4(1):7–12.

    Google Scholar 

  9. Agustin SA, Babu SS. Thyroid segmentation on us medical images: an overview. Int J Emerg Technol Adv Eng. 2012;2(2):398–404.

    Google Scholar 

  10. Poudel P, Illanes A, Arens C, Hansen C, Friebe M. Active contours extension and similarity indicators for improved 3d segmentation of thyroid ultrasound images. Proc SPIE. 2017. https://doi.org/10.1117/12.2254029.

    Google Scholar 

  11. Guo Y, Cheng H, Tian J, Zhang Y. A novel approach to speckle reduction in ultrasound imaging. Ultrasound Med Biol. 2009;35(4):628–40.

    Article  PubMed  Google Scholar 

  12. Guo Y, Xia R, Şengür A, Polat K. A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering. Neural Comput Appl. 2016. https://doi.org/10.1142/S1793005711001858.

    Google Scholar 

  13. Akbulut Y, Sengür A, Guo Y. Texture segmentation based on Gabor filters and neutrosophic graph cut. In: International conference on advanced technology & sciences (ICAT’16), Konya, Turkey, 1–3 September 2016.

Download references

Acknowledgements

This project was supported by Harbin medical university scientific research innovation fund (NO2016LCZX08), and Health and family planning commission of Heilongjiang province scientific research project (NO2014-308).

Publisher’s Note

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

Author information

Affiliations

  1. Department of Computer Science, University of Illinois at Springfield, Springfield, IL, USA

    Yanhui Guo

  2. Department of Ultrasound, Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, China

    Shuang-Quan Jiang & Jia-Wei Tian

  3. School of Management, Harbin Institute of Technology, Harbin, Heilongjiang, China

    Baiqing Sun

  4. Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC, Australia

    Siuly Siuly

  5. Department of Electrical and Electronics Engineering, Firat University, Elazig, Turkey

    Abdulkadir Şengür

Authors
  1. Yanhui Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuang-Quan Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Baiqing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Siuly Siuly
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abdulkadir Şengür
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jia-Wei Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanhui Guo.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Guo, Y., Jiang, SQ., Sun, B. et al. Using neutrosophic graph cut segmentation algorithm for qualified rendering image selection in thyroid elastography video. Health Inf Sci Syst 5, 8 (2017). https://doi.org/10.1007/s13755-017-0032-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13755-017-0032-y

Keywords

  • Neutrosophic set
  • Graph cut
  • Indeterminate filtering
  • Elastogram
  • Thyroid ultrasound