A Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms

Journal of Digital Imaging volume 32, pages728–745(2019)Cite this article

Abstract

Breast cancer is the most common cancer diagnosed in women worldwide. Up to 50% of non-palpable breast cancers are detected solely through microcalcification clusters in mammograms. This article presents a novel and completely automated algorithm for the detection of microcalcification clusters in a mammogram. A multiscale 2D non-linear energy operator is proposed for enhancing the contrast between the microcalcifications and the background. Several texture, shape, intensity, and histogram of oriented gradients (HOG)–based features are used to distinguish microcalcifications from other brighter mammogram regions. A new majority class data reduction technique based on data distribution is proposed to counter data imbalance problem. The algorithm is able to achieve 100% sensitivity with 2.59, 1.78, and 0.68 average false positives per image on Digital Database for Screening Mammography (scanned film), INbreast (direct radiography) database, and PGIMER-IITKGP mammogram (direct radiography) database, respectively. Thus, it might be used as a second reader as well as a screening tool to reduce the burden on radiologists.

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

Access options

Buy single article

Instant access to the full article PDF.

US$ 39.95

Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

References

  1. 1.

    Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A: Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68 (6): 394–424, 2018

    Article  Google Scholar 

  2. 2.

    Bulas D, Shah N: International pediatric radiology education: who should be trained, and how? Pediatr Radiol 44 (6): 639–641, 2014. https://doi.org/10.1007/s00247-014-2910-7

    PubMed  Article  Google Scholar 

  3. 3.

    Chen Z, Strange H, Oliver A, Denton ERE, Boggis C, Zwiggelaar R: Topological modeling and classification of mammographic microcalcification clusters. IEEE Trans Biomed Eng 62 (4): 1203–1214, 2015. https://doi.org/10.1109/TBME.2014.2385102

    PubMed  Article  Google Scholar 

  4. 4.

    Cheng H, Cai X, Chen X, Hu L, Lou X: Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recogn 36 (12): 2967–2991, 2003. https://doi.org/10.1016/S0031-3203(03)00192-4

    Article  Google Scholar 

  5. 5.

    Ciecholewski M: Microcalcification segmentation from mammograms: a morphological approach. J Digit Imaging 30 (2): 172–184, 2017

    PubMed  Article  Google Scholar 

  6. 6.

    Cox RF, Hernandez-Santana A, Ramdass S, McMahon G, Harmey JH, Morgan MP: Microcalcifications in breast cancer: novel insights into the molecular mechanism and functional consequence of mammary mineralisation. Br J Cancer 106 (3): 525–537, 2012

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Cristianini N, Shawe-Taylor J: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods Cambridge: Cambridge University Press, 2000

    Google Scholar 

  8. 8.

    Dalal N, Triggs B: Histograms of oriented gradients for human detection.. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol 1. IEEE, 2005, pp 886–893

  9. 9.

    Dengler J, Behrens S, Desaga J: Segmentation of microcalcifications in mammograms. IEEE Trans Med Imaging 12 (4): 634–642, 1993. https://doi.org/10.1109/42.251111

    CAS  PubMed  Article  Google Scholar 

  10. 10.

    Ding C, Peng H: Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3 (02): 185–205, 2005

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    El-Naqa I, Yang Y, Wernick MN, Galatsanos NP, Nishikawa RM: A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging 21 (12): 1552–1563, 2002

    PubMed  Article  Google Scholar 

  12. 12.

    Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136 (5): E359–E386, 2015. https://doi.org/10.1002/ijc.29210

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Gonzalez RC, Woods RE: Digital Image Processing, 2nd edition. Upper Saddle River: Prentice-Hall, 2002

    Google Scholar 

  14. 14.

    Guan PP, Yan H: A hierarchical multilevel thresholding method for edge information extraction using fuzzy entropy. Int J Mach Learn Cybern 3 (4): 297–305, 2012

    Article  Google Scholar 

  15. 15.

    Guo Y, Dong M, Yang Z, Gao X, Wang K, Luo C, Ma Y, Zhang J: A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified pcnn. Comput Methods Programs Biomed 130: 31–45, 2016

    PubMed  Article  Google Scholar 

  16. 16.

    Gurcan M, Yardimci Y, Cetin A, Ansari R: Detection of microcalcifications in mammograms using higher order statistics. IEEE Signal Process Lett 4 (8): 213–216, 1997. https://doi.org/10.1109/97.611278

    Article  Google Scholar 

  17. 17.

    Haralick RM, Shanmugam K, Dinstein I: Textural features for image classification. IEEE Trans Syst Man Cybern 3 (6): 610–622, 1973

    Article  Google Scholar 

  18. 18.

    Kaiser JF: On a simple algorithm to calculate the energy of a signal.. In: International Conference on Acoustics, Speech, and Signal Processing, ICASSP-90. IEEE, 1990, pp 381–384

  19. 19.

    Kallergi M, Carney GM, Gaviria J: Evaluating the performance of detection algorithms in digital mammography. Med Phys 26 (2): 267–275, 1999. http://scitation.aip.org/content/aapm/journal/medphys/26/2/10.1118/1.598514

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Karale VA, Mukhopadhyay S, Singh T, Khandelwal N, Sadhu A: Automated detection of microcalcification clusters in mammograms.. In: SPIE Medical Imaging, vol 10134, 2017, pp 101342r–101342r. International society for optics and photonics. https://doi.org/10.1117/12.2254330

  21. 21.

    Kim JK, Park HW: Statistical textural features for detection of microcalcifications in digitized mammograms. IEEE Trans Med Imaging 18 (3): 231–238, 1999. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=764896

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Linguraru MG, Marias K, English R, Brady M: A biologically inspired algorithm for microcalcification cluster detection. Med Image Anal 10 (6): 850–862, 2006. http://www.sciencedirect.com/science/article/pii/S1361841506000624

    PubMed  Article  Google Scholar 

  23. 23.

    Liu X, Mei M, Liu J, Hu W: Microcalcification detection in full-field digital mammograms with pfcm clustering and weighted svm-based method. EURASIP Journal on Advances in Signal Processing 2015 (1): 1, 2015

    Article  Google Scholar 

  24. 24.

    Mordang JJ, Janssen T, Bria A, Kooi T, Gubern-Mérida A, Karssemeijer N: Automatic microcalcification detection in multi-vendor mammography using convolutional neural networks.. In: International Workshop on Digital Mammography. Springer, 2016, pp 35–42

  25. 25.

    Mukhopadhyay S, Ray G: A new interpretation of nonlinear energy operator and its efficacy in spike detection. IEEE Trans Biomed Eng 45 (2): 180–187, 1998

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Nakayama R, Uchiyama Y, Yamamoto K, Watanabe R, Namba K: Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms. IEEE Trans Biomed Eng 53 (2): 273–283, 2006

    PubMed  Article  Google Scholar 

  27. 27.

    Nam SH, Choi JY: A method of image enhancement and fractal dimension for detection of microcalcifications in mammogram.. In: Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol 2. IEEE, 1998, pp 1009–1012. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=745620

  28. 28.

    Oliver A, Torrent A, Lladó X, Tortajada M, Tortajada L, Sentís M, Freixenet J, Zwiggelaar R: Automatic microcalcification and cluster detection for digital and digitised mammograms. Knowl-Based Syst 28: 68–75, 2012 . http://www.sciencedirect.com/science/article/pii/S0950705111002577

    Article  Google Scholar 

  29. 29.

    Papadopoulos A, Fotiadis DI, Costaridou L: Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. Comput Biol Med 38 (10): 1045–1055, 2008. http://www.sciencedirect.com/science/article/pii/S0010482508001042

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Peng R, Chen H, Varshney PK: Noise-enhanced detection of micro-calcifications in digital mammograms. IEEE J Sel Top Sign Proces 3 (1): 62–73, 2009. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4786547

    Article  Google Scholar 

  31. 31.

    Rampun A, Wang H, Scotney B, Morrow P, Zwiggelaar R: Classification of mammographic microcalcification clusters with machine learning confidence levels.. In: 14Th International Workshop on Breast Imaging (IWBI 2018), vol 10718, 2018, p 107181b. International society for optics and photonics

  32. 32.

    Rangayyan RM, Ayres FJ, Desautels JEL: A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. J Franklin Inst 344: 312–348, 2007

    Article  Google Scholar 

  33. 33.

    Rose C, Turi D, Williams A, Wolstencroft K, Taylor C: Web services for the DDSM and digital mammography research.. In: Proceedings of the 8th International Conference on Digital Mammography, IWDM’06. Springer, Berlin, 2006, pp 376–383, https://doi.org/10.1007/11783237_51

  34. 34.

    Seth S, Mukhopadhyay S: Multi-level thresholding-based breast segmentation in mammograms.. In: International Conference on Communication, Computers and Devices, Kharagpur, India, 2010

  35. 35.

    Shen L, Rangayyan RM, Desautels JL: Shape analysis of mammographic calcifications.. In: Fifth Annual IEEE Symposium on Computer-Based Medical Systems. IEEE, 1992, pp 123–128

  36. 36.

    Shin S, Lee S, Yun ID: Classification based micro-calcification detection using discriminative restricted Boltzmann machine in digitized mammograms.. In: SPIE Medical Imaging, 2014, pp 90351l–90351l. International society for optics and photonics

  37. 37.

    Soltanian-Zadeh H, Rafiee-Rad F, Pourabdollah-Nejad DS: Comparison of multiwavelet, wavelet, haralick, and shape features for microcalcification classification in mammograms. Pattern Recogn 37 (10): 1973–1986, 2004. http://www.sciencedirect.com/science/article/pii/S0031320304001323

    Article  Google Scholar 

  38. 38.

    Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genetic Epidemiology: the Official Publication of the International Genetic Epidemiology Society 31 (4): 306–315, 2007

    Article  Google Scholar 

  39. 39.

    Wei L, Yang Y, Nishikawa RM, Vernick MN, Edwards A: Relevance vector machine for automatic detection of clustered microcalcifications. IEEE Trans Med Imaging 24 (10): 1278–1285, 2005

    PubMed  Article  Google Scholar 

  40. 40.

    Wilkinson L, Thomas V, Sharma N: Microcalcification on mammography: approaches to interpretation and biopsy. Br J Radiol 90 (1069): 20160594, 2016

    PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Woods KS, Solka JL, Priebe CE, Doss CC, Bowyer KW, Clarke LP Comparative evaluation of pattern recognition techniques for detection of microcalcifications. Int J Pattern Recognit Artif Intell 841–852, 1993. https://doi.org/10.1117/12.148696

  42. 42.

    Yu S, Brown S, Xue Y, Guan L: Enhancement and identification of microcalcifications in mammogram images using wavelets.. In: IEEE International Conference on Systems, Man, and Cybernetics, vol 2, 1996, pp 1166–1171, https://doi.org/10.1109/ICSMC.1996.571251

  43. 43.

    Yu S, Guan L: A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE Trans Med Imaging 19 (2): 115–126, 2000

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Zhang X, Homma N, Goto S, Kawasumi Y, Ishibashi T, Abe M, Sugita N, Yoshizawa M A hybrid image filtering method for computer-aided detection of microcalcification clusters in mammograms. Journal of Medical Engineering 2013, 2013. http://www.hindawi.com/journals/jme/2013/615254/abs/

Download references

Author information

Affiliations

  1. Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, 721302, India

    Vikrant A. Karale, Joshua P. Ebenezer & Sudipta Mukhopadhyay

  2. Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

    Jayasree Chakraborty

  3. Department of Radiodiagnosis and Imaging, Post-graduate Institute of Medical Education and Research, Chandigarh, 160012, India

    Tulika Singh & Niranjan Khandelwal

  4. EKO CT & MRI Scan Center, Kolkata Medical College, Kolkata, 700004, India

    Anup Sadhu

Authors
  1. Vikrant A. Karale
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joshua P. Ebenezer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jayasree Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tulika Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anup Sadhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Niranjan Khandelwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sudipta Mukhopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sudipta Mukhopadhyay.

Additional information

Publisher’s Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

(PDF 143 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Karale, V.A., Ebenezer, J.P., Chakraborty, J. et al. A Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms. J Digit Imaging 32, 728–745 (2019). https://doi.org/10.1007/s10278-019-00249-5

Download citation

Keywords

  • 2D NEO
  • Non-linear energy operator
  • NEO
  • Microcalcification
  • Microcalcification clusters
  • Mammogram
  • Shape features
  • Texture features
  • SVM classifier