Skip to main content

Advertisement

SpringerLink
Log in

A breast tumors segmentation and elimination of pectoral muscle based on hidden markov and region growing

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

Abstract

In this article, we propose an automatic method for the detection and segmentation of the tumor on mammogram images. Most methods of detection of a tumor require an extraction of a large number of texture features from multiple calculations. The study first examines a technique of pre-processing images to obtain the Otsu thresholding method which eliminate items that do not belong in. After performing the thresholding, we estimate the number of base classes of technical LBP (Local Binary Pattern). To automate the initialization task, the classification proposed by applying dynamic k-means and improve the classes obtained by the method of Markov. Then we calculate the correlation between these classes and the original image, we deduce the class that contains the tumor and pectoral muscle. Finally, it uses the method of growing the region to eliminate pectoral muscle. The result obtained by this approach shows the quality and accuracy of extracting parts of the tumor compared to existing approaches in the literature.

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

Similar content being viewed by others

References

  1. Agrawal P, Vatsa M, Singh R (2014) Saliency based mass detection from screening mammograms. Signal Process 99:29–47

    Article  Google Scholar 

  2. Alayli RM, El-Zaar AY (2013) An iterative mammographic image thresholding algorithm for breast cancer detection. ACIT'2013

  3. Aschwanden P, Guggenbuhl W (1992) Experimental results from a comparative study on correlation-type registration algorithms. Robust Computer Vision, 268–289

  4. Bon AT (2009) Developing K-Means Clustering on Beltline Moulding Contours. J Appl Sci Res 5(5):2189–2193

    Google Scholar 

  5. Boss R, Thangavel K, Daniel D (2013) Automatic mammogram image breast region extraction and removal of pectoral muscle. arXiv preprint arXiv:1307.7474

  6. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  Google Scholar 

  7. Chen Z, Zwiggelaar R (2010) Segmentation of the Breast Region with Pectoral Muscle Removal in Mammograms. Medical Image Understanding and Analysis (MIUA) 2010. The University of Warwick, Coventry, pp 71–76

    Google Scholar 

  8. David R, Arnau O, Joan M, Marta P, Joan E (2005) Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms, Springer-Verlag, Berlin Heidelberg, 471–478, LNCS 3523

  9. Djukovic D, Zhang J, Raftery D (2018) Colorectal Cancer Detection Using Targeted LC-MS Metabolic Profiling. In: Colorectal Cancer. Humana Press, New York, pp. 229–240

  10. Dokládal P, Lohou C, Perroton L, Bertrand G (1999) Liver blood vessels extraction by a 3-D topological approach. In Medical Image Computing and Computer-Assisted Intervention–MICCAI’99 (pp. 98–105). Springer Berlin/Heidelberg

  11. El Idrissi el Kaitouni S, Abbad A, Tairi H (2017). Tumor extraction and elimination of pectoral muscle based on hidden Markov and region growing: applied based MIAS. In International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). IEEE Conference. In press

  12. El Idrissi el Kaitouni S, Abbad A, Tairi H (2017) Automatic detection of the tumour on mammogram images based on hidden Markov and active contour with quasi-automatic initialisation. International Journal of Medical Engineering and Informatics 9(4):316–331

    Article  Google Scholar 

  13. Elmoufidi A, El Fahssi K, Jai-Andaloussi S, Madrane N, Sekkaki A (2014). Detection of regions of interest's in mammograms by using local binary pattern, dynamic k-means algorithm and gray level co-occurrence matrix. In Next Generation Networks and Services (NGNS), 2014 Fifth International Conference on (pp. 118–123). IEEE

  14. Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6:721–741

    Article  Google Scholar 

  15. Gumaei A, El-Zaart A, Hussien M, Berbar M (2012) Breast segmentation using k-means algorithm with a mixture of gamma distributions. In Broadband Networks and Fast Internet (RELABIRA), 2012 Symposium on (pp. 97–102). IEEE

  16. Jen CC, Yu SS (2015) Automatic detection of abnormal mammograms in mammographic images. Expert Syst Appl 42(6):3048–3055

    Article  Google Scholar 

  17. Kwok SM, Chandrasekhar R, Attikiouzel Y, Rickard MT (2004) Automatic pectoral muscle segmentation on mediolateral oblique view mammograms. IEEE Trans Med Imaging 23(9):1129–1140

    Article  Google Scholar 

  18. Liu L, Wang J, Wang T (2011) Breast and Pectoral Muscle Contours Detection Based on Goodness of Fit Measure, 978–1–4244-5089-3/11/$26.00. IEEE

  19. Liu CC, Tsai CY, Tsui TS, Yu SS (2012) An improved GVF snake based breast region extrapolation scheme for digital mammograms. Expert Syst Appl 39(4):4505–4510

    Article  Google Scholar 

  20. Margolies LR, Salvatore M, Yip R et al (2018) The chest radiologist's role in invasive breast cancer detection. Clin Imaging 50:13–19

    Article  Google Scholar 

  21. Mohanty AK, Sahoo S, Pradhan A, Lenka SK (2011) Detection of masses from mammograms using mass shape pattern. International Journal of Computer Technology and Applications 2(4):1131–1139

  22. Mustra M, Grgic M (2013) Robust automatic breast and pectoral muscle segmentation from scanned mammograms. Signal Process 93(10):2817–2827

    Article  Google Scholar 

  23. Mustra M, Bozek J, Grgic M (2009) Breast Border Extraction and Pectoral Muscle Detection using Wavelet Decomposition. EUROCON, IEEE, St. Petersburg, pp 1426–1433

    Google Scholar 

  24. Nagi J, Kareem SA, Nagi F, Ahmed SK (2010) Automated breast profile segmentation for ROI detection using digital mammograms. In Biomedical Engineering and Sciences (IECBES), 2010 I.E. EMBS Conference on (pp. 87–92). IEEE

  25. Nakajima T, Yasufuku K (2018) Early lung cancer: methods for detection. In: Interventions in Pulmonary Medicine. Springer, Cham, pp. 245–256

  26. Nie L, Wang M, Zhang L, Yan S, Zhang B, Chua TS (2015) Disease inference from health-related questions via sparse deep learning. IEEE Trans Knowl Data Eng 27(8):2107–2119

    Article  Google Scholar 

  27. Nie L, Zhang L, Yang Y, Wang M, Hong R, Chua TS (2015) Beyond doctors: Future health prediction from multimedia and multimodal observations. In Proceedings of the 23rd ACM international conference on Multimedia (pp. 591–600). ACM

  28. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59

    Article  Google Scholar 

  29. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  30. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics 9(1):62–66

    Article  Google Scholar 

  31. Ray S, Turi RH (1999) Determination of number of clusters in k-means clustering and application in colour image segmentation. In Proceedings of the 4th international conference on advances in pattern recognition and digital techniques (pp. 137–143)

  32. Selle D, Preim B, Schenk A, Peitgen HO (2002) Analysis of vasculature for liver surgical planning. IEEE Trans Med Imaging 21(11):1344–1357

    Article  Google Scholar 

  33. Singh N, Mohapatra AG, Kanungo G (2011) Breast cancer mass detection in mammograms using K-means and fuzzy C-means clustering. Int J Comput Appl 22(2):0975–8887

    Google Scholar 

  34. Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, … Taylor P (1994) The mammographic image analysis society digital mammogram database. In ExerptaMedica. International Congress Series (Vol. 1069, pp. 375–378)

  35. Wang L, Zhu ML, Deng LP, Yuan X (2010) Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model. Journal of Zhejiang University-SCIENCE C 11(2):111–118

    Article  Google Scholar 

  36. 1Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20(1):45–57

Download references

Author information

Authors and Affiliations

  1. LIIAN, Department of Computer Science, Faculty of Sciences Dhar El Mahraz, University Sidi Mohamed Ben Abdelah, BP 1796, Fez, Morocco

    Soukaina El Idrissi El Kaitouni, Abdelghafour Abbad & Hamid Tairi

Authors
  1. Soukaina El Idrissi El Kaitouni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdelghafour Abbad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamid Tairi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soukaina El Idrissi El Kaitouni.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El Idrissi El Kaitouni, S., Abbad, A. & Tairi, H. A breast tumors segmentation and elimination of pectoral muscle based on hidden markov and region growing. Multimed Tools Appl 77, 31347–31362 (2018). https://doi.org/10.1007/s11042-018-6089-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6089-z

Keywords

Search

Navigation