Skip to main content

Advertisement

SpringerLink
Log in

Iterative quadtree decomposition based automatic selection of the seed point for ultrasound breast tumor images

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Based on seed region growing method, lesion segmentation for ultrasound breast tumor images often requires manual selection of the seed point, which is both time-consuming and laborious. To overcome this limit, this paper attempts to explore an automatic method for finding the seed point inside the tumor. Two criteria combining iterative quadtree decomposition (QTD) and the gray characteristics of the lesion are thus designed to locate the seed point. One is to seek the biggest homogenous region and the other is to select the seed region where the seed point is found. Furthermore, this study validates the proposed algorithm through 110 ultrasonic breast tumor images (including 58 malignant tumor images and 52 benign tumor images). According to the needs of the seed region growing algorithm, if the seed point is found inside the tumor, it means the proposed method is correct. Otherwise, it means that the method is a failure. As the quantitative experiment results show, the proposed method in this paper can automatically find the seed point inside the tumor with an accuracy rate of 97.27 %.

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

Similar content being viewed by others

References

  1. Adams R, Bischof L (1994) Seeded region growing. Pattern Anal Mach Intell 16:641–647

    Article  Google Scholar 

  2. Chen D, Chang R, Chen C, Ho M, Kuo S, Chen S, Hung S, Moon WK (2005) Classification of breast ultrasound images using fractal feature. Clin Imaging 29:235–245

    Article  Google Scholar 

  3. Cheng HD, Shan J, Wen J, Guo Y, Zhan L (2010) Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recogn 43:299–317

    Article  MATH  Google Scholar 

  4. El-Harby AA, Behery GM (2008) Qualitative image compression algorithm relying on quadtree, International Journal on Graphics. Vis Image Process 8:41–50

    Google Scholar 

  5. Felzenszwalb PF, Huttenlocher DP (1998) Image segmentation using local variation. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 98–104

  6. Gholinezhad S, Jamshidi S, Hajizadeh A (2015) Quad-Tree decomposition method for areal upscaling of heterogeneous reservoirs: application to arbitrary shaped reservoirs. Fuel 13:9659–670

    Google Scholar 

  7. Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, Inc, Upper Saddle River

    Google Scholar 

  8. Guo Y, Şengür A, Tian J-W (2016) A novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set. Comput Methods Programs Biomed 123:43–53

    Article  Google Scholar 

  9. Hojjatoleslami SA, Kittler J (1998) Region growing: a new approach. IEEE Trans Image Process 7(7):1079–1084

    Article  Google Scholar 

  10. Huang QH, Lee SY, Liu LZ, Lu MH, Jin LW, Li AH (2015) A robust graph-based segmentation method for breast tumors in ultrasound images. Ultrasound Med Biol 41(6):1737–1748

    Article  Google Scholar 

  11. Kaur M, Kaur J, Kaur J (2011) Survey of contrast enhancement techniques based on histogram equalization. Int J Adv Comput Sci Appl 2:138–141

    MATH  Google Scholar 

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

    Article  Google Scholar 

  13. Ma F, Bajger M, Slavotinek JP, Bottema MJ (2007) Two graph theory based methods for identifying the pectoral muscle in mammograms. Pattern Recogn 40:2592–2602

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Moi Hoon Y, Edirisinghe EA, Bez HE (2008) A novel algorithm for initial lesion detection in ultrasound breast images. J Appl Clin Med Phys 9:181–199

    Google Scholar 

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

    Article  Google Scholar 

  17. Peng G, Lee W-M, Roubidoux MA (2016) Automated 3D ultrasound image segmentation to aid breast cancer image interpretation. Ultrasonics 65:51–58

    Article  Google Scholar 

  18. Pisani P, Parkin DM, Ferlay J (1993) Estimates of the worldwide mortality from eighteen major cancers in 1985. Implications for prevention and projections of future burden. Int J Cancer J Int Cancer 55:891–903

    Article  Google Scholar 

  19. Poonguzhali S, Ravindran G (2006) A complete automatic region growing method for segmentation of masses on ultrasound images. Int Conf Biomed Pharm Eng 88–92

  20. Shan J, Cheng HD, Wang Y (2008) A novel automatic seed point selection algorithm for breast ultrasound images. 19th International Conference on Pattern Recognition 1–4

  21. Shan J, Cheng HD, Wang Y (2012) Completely automated segmentation approach for breast ultrasound images using multiple-domain features. Ultrasound Med Biol 38:262–275

    Article  Google Scholar 

  22. Yu Y, Acton ST (2002) Speckle reducing anisotropic diffusion. IEEE Trans Image Process 11:1260–1270

    Article  MathSciNet  Google Scholar 

  23. Zhai G, Lin W, Cai J, Yang X, Zhang W (2009) Efficient quadtree based block-shift filtering for deblocking and deranging. J Vis Commun Image Represent 20:595–607

    Article  Google Scholar 

  24. Zhang H, Foo SW, Krishnan SM, Thng CH (2004) Automated breast masses segmentation in digitized mammograms. IEEE Int Workshop Biomed Circuits Syst

Download references

Acknowledgments

This work is supported in part by the National Basic Research Program of China (2011CB707504), Colleges and universities domestic visiting scholar project of young backbone teachers in Shandong province and Youth fund projects of Jining Medical University.

Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Modern Optics System, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Huaiyu Fan & Junshan Ma

  2. Department of Medical Information Engineering, Jining Medical University, Shandong, 272067, China

    Huaiyu Fan

  3. Taian City Central Hospital, Shandong, 271000, China

    Huaihai Fan

  4. SIAT, Chinese Academy of Science, Shenzhen, 518055, China

    Zhihan Lv

Authors
  1. Huaiyu Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junshan Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huaihai Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhihan Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Junshan Ma or Zhihan Lv.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, H., Ma, J., Fan, H. et al. Iterative quadtree decomposition based automatic selection of the seed point for ultrasound breast tumor images. Multimed Tools Appl 76, 3505–3517 (2017). https://doi.org/10.1007/s11042-016-3761-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3761-z

Keyword

Search

Navigation