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McCulloch’s algorithm inspired cuckoo search optimizer based mammographic image segmentation

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Abstract

Multi-level thresholding for mammogram image segmentation leads to much better sub-sections of the intensity span, and hence very useful in breast cancer detection. In order to segment the mammogram image efficiently, in this paper, three popular nature inspired algorithms namely Harmony Search Algorithm (HSA), Electro-magnetism Optimization (EMO) and McCulloch’s Algorithm inspired Cuckoo Search Optimization algorithm (MACSO) are studied in detail; and are employed for desired cost function maximization for two well-known multi-level thresholding methods like Otsu and Kapur efficiently. The proposed approach is applied to all the 322 test images of database presented by Mammographic Image Analysis Society (MIAS), to detect pectoral muscle, breast and suspicious mass efficiently. Performance of EMO, MACSO and HSA were analysed using measures like best fitness, MSE, PSNR, SSIM and TIME. From the experimental results, it is concluded that MACSO with Otsu was found to be robust for segmentation of mammogram images accurately.

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References

  1. Avuti S, Bajaj V, Kumar A et al (2019) A novel pectoral muscle segmentation from scanned mammograms using EMO algorithm. Biomed Eng Lett 9:481–496. https://doi.org/10.1007/s13534-019-00135-7

    Article  Google Scholar 

  2. Bhandari AK, Singh VK, Kumar A, Singh GK (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41(7):3538–3560

    Article  Google Scholar 

  3. Bhandari AK, Kumar A, Chaudhary S, Singh GK (2016) A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms. Expert Syst Appl 63:112–133

    Article  Google Scholar 

  4. Birbil Sİ, Fang S-C (2003) An electromagnetism-like mechanism for global optimization. Journal of Global Optimization 25:263–282

    Article  MathSciNet  Google Scholar 

  5. Chambers JM, Mallows CL, Stuck B (1976) A method for simulating stable random variables. J Am Stat Assoc 71(354):340–344

    Article  MathSciNet  Google Scholar 

  6. Cowan EW (1968) Basic electromagnetism. Academic Press, New York

    Google Scholar 

  7. Dey S, Bhattacharyya S, Maulik U (2016) New quantum inspired metaheuristic techniques for multi-level colour image thresholding. Appl Soft Comput 46:677–702

    Article  Google Scholar 

  8. Ferrari RJ, Rangayyan RM, Desautels JEL, Frère AF (2001) Segmentation of mammograms: Identification of the skin boundary, pectoral muscle, and fibroglandular disc, in IWDM 2000: Proc. 5th Int. Workshop Digital Mammography, M. J. Yaffe, Ed., Madison, WI, 2001, pp. 573–579.

  9. Gupta R, Undrill PE (1995) The use of texture analysis to delineate suspicious masses in mammography. Phys Med Biol 40:835–855

    Article  Google Scholar 

  10. Hatanaka Y, Hara T, Fujita H, Kasai S, Endo T, Iwase T (Dec. 2001) Development of an automated method for detecting mammographic masses with a partial loss of region. IEEE Trans Med Imag 25:5209–1212

    Google Scholar 

  11. M. Heath, K. Bowyer, D. Kopans, R. Moore, W.P. Kegelmeyer, The digital database for screening Mammography, in: Proceedings of the Fifth International Workshop on Digital Mammography, IWDM 2000, Medical Physics Publishing, 2001, pp. 212–218.

  12. Horng M (2011) Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Syst Appl 38:13785–13791

    Google Scholar 

  13. Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding usingthe entropy of the histogram. Computer Vision Graphics Image Processing 2:273–285

    Article  Google Scholar 

  14. Karnan M, Thangavel K (2007) Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of micro calcifications. Comput Methods Prog Biomed 87(1):12–20

    Article  Google Scholar 

  15. Karssemeijer N (Feb. 1998) Automated classification of parenchymal patterns in mammograms. Phys Med Biol 43(2):365–378

    Article  Google Scholar 

  16. Kumar AS, Kumar A, Bajaj V et al. (2018) Fractional-order Darwinian swarm intelligence inspired multilevel thresholding for mammogram segmentation. 2018 International Conference on Communication and Signal Processing (ICCSP), Chennai, pp. 0160–0164. https://doi.org/10.1109/ICCSP.2018.8524302.

  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. Leccardi M (2005) Comparison of three algorithms for levy noise generation. In Proceedings of fifth euromech nonlinear dynamics conference

  19. Maitra IK, Nag S, Bandyopadhyay SK (2011) Technique for pre-processing of digital mammogram, computer methods and programs in biomedicine, Available online from: http://www.sciencedirect.Com/science/article/pii/S0169260711001222S, 12 June 2011.

  20. Meyer F, Beucher S (1990) Morphological segmentation. J Vis Commun Image Represent 1:21–46

    Article  Google Scholar 

  21. Moghbel M, Ooi CY, Ismail N, Hau YW, Memari N (2019) A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography. Artif Intell Rev 53:1873–1918. https://doi.org/10.1007/s10462-019-09721-8

    Article  Google Scholar 

  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. Oliva D, Cuevas E, Pajares G, Zaldivar D, Perez-Cisneros M (2013) Multilevel thresholding segmentation based on harmony search optimization. J Appl Math 1–24

  24. Olivaa D, Cuevas E, Pajares G, Zaldivar D, Osuna V (2014) A multilevel thresholding algorithm using electromagnetism optimization. Neurocomputing 139:357–381

    Article  Google Scholar 

  25. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybernetics SMC-9:62–66

    Article  Google Scholar 

  26. Ouadfel S, Taleb Ahmed A (2016) Social spiders optimization and flower pollination algorithm for multilevel image thresholding: a performance study. Expert Syst Appl 55:566–584

    Article  Google Scholar 

  27. Pal SK, Bhandari D, Kundu MK (1994) Genetic algorithms, for optimal image enhancement. Pattern Recognition Lett 15:261–271

    Article  Google Scholar 

  28. D.Raba, A.Oliver, J.Marti, M.Peracaula, J.Espunya, Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms, vol.LNCS3523, Springer-Verlag, Berlin 2005 (pp. 471–478).

  29. Rampun A, López-Linares K, Morrow PJ, Scotney BW, Wang H, Ocaña IG, Maclair G, Zwiggelaar R, González Ballester MA, Macía I (2019) Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network. Med Image Anal 57:1–17

    Article  Google Scholar 

  30. Saha PK, Udupa JK, Conant EF, Chakraborty DP, Sullivan D (Aug. 2001) Breast tissue density quantification via digitized mammograms. IEEE Trans Med Imag 20:792–803

    Article  Google Scholar 

  31. Sahoo PK, Soltani SAKC, Wong AKC (1988) A survey of thresholding techniques. Computer Vision, Graphics, and Image Processing 41.2:233–260

    Article  Google Scholar 

  32. Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–165

    Article  Google Scholar 

  33. Shen R, Yan K, Xiao F, Chang J, Jiang C, Zhou K (2018) Automatic pectoral muscle region segmentation in mammograms using genetic algorithm and morphological selection. J Digit Imaging 31:680–691. https://doi.org/10.1007/s10278-018-0068-9

    Article  Google Scholar 

  34. Singh H, Kumar A, Balyan LK, Singh GK (2017) Regionally equalized and contextually clipped gamma correction approach for dark image enhancement," 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, pp. 431–436.

  35. Singh H, Kumar A, Balyan LK, Singh GK (2019) A novel optimally weighted framework of piecewise gamma corrected fractional order masking for satellite image enhancement. Comput Electrical Eng 75:245–261

    Article  Google Scholar 

  36. Suckling J, Parker J, Dance DR, Astley A, Hutt I, Boggis CRM, Ricketts I, Stamatakis E, Cernaez N, Kok SL, Taylor P, Betal D, Savage J (1994) The mammographic image analysis society digital mammogram database, in: Proceedings of the Second International Workshop on Digital Mammography, York, England, 10–12 July 1994, pp. 375–378.

  37. Suresh S, Lal S (2016) An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions. Expert Syst Appl 58:184–209

    Article  Google Scholar 

  38. Taghanaki SA, Liu Y, Miles B, Hamarneh G (Nov. 2017) Geometry-based pectoral muscle segmentation from MLO mammogram views. IEEE Trans Biomed Eng 64(11):2662–2671. https://doi.org/10.1109/TBME.2017.2649481

    Article  Google Scholar 

  39. Tomas JS (2011) Segmentation of the breast region with pectoral muscle suppression and automatic breast density classification, Universite´ Catholique de Louvain, A Thesis Submitted for the Degree of Master In ge’nieur Civil E’lectricien

  40. Tsai W-H (1985) Moment-preserving thresolding: a new approach. Computer Vision, Graphics, and Image Processing 29(3):377–393

    Article  Google Scholar 

  41. Tzikopoulos SD, Mavroforakis ME, Georgiou HV, Dimitropoulos N, Theodoridis S (2011) A fully, automated scheme for mammographic segmentation and classification based on breast density and asymmetry. Computer Methods and Programs in Biomedicine 102(1):47–63

    Article  Google Scholar 

  42. Yang XS, Deb S (2009) Cuckoo search via levey flights. Proceedings of the world congress on nature and biologically inspired computing NABIC: Coimbatore 4:210–214

    Article  Google Scholar 

  43. Yapa RD, Harada K (2007) Breast skin-line estimation and breast segmentation in mammograms using fast-marching method. International Journal of Biological and Life Sciences 3(1):54–62

    Google Scholar 

  44. Yin K, Yan S, Song C, Zheng B (2019) A robust method for segmenting pectoral muscle in mediolateral oblique (MLO) mammograms. Int J CARS 14:237–248. https://doi.org/10.1007/s11548-018-1867-7

    Article  Google Scholar 

  45. Zuiderveld K (1994) Contrast limited adaptive histogram equalization, Graphic Gems IV, Academic Press Professional, San Diego, (pp. 474–485)

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Authors and Affiliations

  1. PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, 482005, India

    Kumar A. Santhos, A. Kumar & V. Bajaj

  2. Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India

    G. K. Singh

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  1. Kumar A. Santhos
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  2. A. Kumar
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  3. V. Bajaj
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  4. G. K. Singh
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Correspondence to Kumar A. Santhos.

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Highlights

• Nature inspired algorithms along with multilevel thresholding is used to segment the mammogram images.

• EMO, HSA, and MACSO are employed here for desired multi-level thresholding.

• Otsu and Kapur’s entropy were used to perform the multilevel thresholding.

• Various methods were compared using different evaluation matrices.

• MACSO with Otsu objective function gives best result among the used methods.

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Santhos, K.A., Kumar, A., Bajaj, V. et al. McCulloch’s algorithm inspired cuckoo search optimizer based mammographic image segmentation. Multimed Tools Appl 79, 30453–30488 (2020). https://doi.org/10.1007/s11042-020-09310-w

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  • DOI: https://doi.org/10.1007/s11042-020-09310-w

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