Skip to main content
SpringerLink
Log in

Automatic Detection of Malignant Masses in Digital Mammograms Based on a MCET-HHO Approach

  • Chapter
  • First Online:
Applications of Hybrid Metaheuristic Algorithms for Image Processing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 890))

Abstract

Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. R. Agarwal, O. Diaz, X. Lladó, M.H. Yap, R. Martí, Automatic mass detection in mammograms using deep convolutional neural networks. J. Med. Imaging 6(3), 031,409 (2019)

    Google Scholar 

  2. M.A. Al-antari, M.A. Al-masni, M.T. Choi, S.M. Han, T.S. Kim, A fully integrated computer-aided diagnosis system for digital x-ray mammograms via deep learning detection, segmentation, and classification. Int. J. Med. Inform. 117, 44–54 (2018)

    Article  Google Scholar 

  3. E.R. Arce-Santana, A.R. Mejia-Rodriguez, E. Martinez-Peña, A. Alba, M. Mendez, E. Scalco, A. Mastropietro, G. Rizzo, A new probabilistic active contour region-based method for multiclass medical image segmentation. Med. Biol. Eng. Comput. 57(3), 565–576 (2019)

    Article  Google Scholar 

  4. I. Bankman, Handbook of Medical Image Processing and Analysis (Elsevier, 2008)

    Google Scholar 

  5. M. Bari, A. Ahmed, S. Naveed et al., Lungs cancer detection using digital image processing techniques: a review. Mehran Univ. Res. J. Eng. Technol. 38(2), 351–360 (2019)

    Article  Google Scholar 

  6. R. Blanks, R. Given-Wilson, R. Alison, J. Jenkins, M. Wallis, An analysis of 11.3 million screening tests examining the association between needle biopsy rates and cancer detection rates in the English nhs breast cancer screening programme. Clin. Radiol. (2019)

    Google Scholar 

  7. S. Broggi, E. Scalco, M.L. Belli, G. Logghe, D. Verellen, S. Moriconi, A. Chiara, A. Palmisano, R. Mellone, C. Fiorino et al., A comparative evaluation of 3 different free-form deformable image registration and contour propagation methods for head and neck MRI: the case of parotid changes during radiotherapy. Technol. Cancer Res. Treat. 16(3), 373–381 (2017)

    Article  Google Scholar 

  8. K.H. Cha, N. Petrick, A. Pezeshk, C.G. Graff, D. Sharma, A. Badal, A. Badano, B. Sahiner, Reducing overfitting of a deep learning breast mass detection algorithm in mammography using synthetic images, in Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950. (International Society for Optics and Photonics, 2019), p. 1095004

    Google Scholar 

  9. E. Cuevas, M. Cienfuegos, D. ZaldíVar, M. Pérez-Cisneros, A swarm optimization algorithm inspired in the behavior of the social-spider. Exp. Syst. Appl. 40(16), 6374–6384 (2013)

    Article  Google Scholar 

  10. E. Cuevas, A. González, D. Zaldívar, M. Pérez-Cisneros, An optimisation algorithm based on the behaviour of locust swarms. Int. J. Bio-Inspired Comput. 7(6), 402–407 (2015)

    Article  Google Scholar 

  11. E. Cuevas, V. Osuna, D. Oliva et al., Evolutionary Computation Techniques: A Comparative Perspective, vol. 686 (Springer, 2017)

    Google Scholar 

  12. M.P. De Albuquerque, I.A. Esquef, A.G. Mello, Image thresholding using Tsallis entropy. Pattern Recogn. Lett. 25(9), 1059–1065 (2004)

    Article  Google Scholar 

  13. M.A. Díaz-Cortés, N. Ortega-Sánchez, S. Hinojosa, D. Oliva, E. Cuevas, R. Rojas, A. Demin, A multi-level thresholding method for breast thermograms analysis using dragonfly algorithm. Infrared Phys. Technol. 93, 346–361 (2018)

    Article  Google Scholar 

  14. J.O.B. Diniz, P.H.B. Diniz, T.L.A. Valente, A.C. Silva, A.C. de Paiva, M. Gattass, Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks. Comput. Methods Program. Biomed. 156, 191–207 (2018)

    Article  Google Scholar 

  15. M. Dorigo, G. Di Caro, Ant colony optimization: a new meta-heuristic, in Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol. 2 (IEEE, 1999), pp. 1470–1477

    Google Scholar 

  16. M.A. El Aziz, A.A. Ewees, A.E. Hassanien, Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Exp. Syst. Appl. 83, 242–256 (2017)

    Article  Google Scholar 

  17. L. He, S. Huang, Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing 240, 152–174 (2017)

    Article  Google Scholar 

  18. M. Heath, K. Bowyer, D. Kopans, R. Moore, W.P. Kegelmeyer, The digital database for screening mammography, in Proceedings of the 5th International Workshop on Digital Mammography (Medical Physics Publishing, 2000), pp. 212–218

    Google Scholar 

  19. A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris Hawks optimization: algorithm and applications. Future Gener. Comput. Syst. 97, 849–872 (2019)

    Article  Google Scholar 

  20. E.L. Henriksen, J.F. Carlsen, I.M. Vejborg, M.B. Nielsen, C.A. Lauridsen, The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review. Acta Radiol. 60(1), 13–18 (2019)

    Article  Google Scholar 

  21. S. Hinojosa, D. Oliva, E. Cuevas, M. Pérez-Cisneros, G. Pájares, Real-time video thresholding using evolutionary techniques and cross entropy, in 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) (IEEE, 2018), pp. 1–8

    Google Scholar 

  22. M. Hmida, K. Hamrouni, B. Solaiman, S. Boussetta, Mammographic mass segmentation using fuzzy contours. Comput. Methods Program. Biomed. 164, 131–142 (2018)

    Article  Google Scholar 

  23. J.N. Kapur, P.K. Sahoo, A.K. Wong, A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vision Graph. Image Process. 29(3), 273–285 (1985)

    Article  Google Scholar 

  24. J. Kennedy, Particle swarm optimization, in Encyclopedia of Machine Learning (2010), pp. 760–766

    Google Scholar 

  25. V. Kovalevsky, Image segmentation and connected components, in Modern Algorithms for Image Processing (Springer, 2019), pp. 167–186

    Google Scholar 

  26. C.H. Li, C. Lee, Minimum cross entropy thresholding. Pattern Recogn. 26(4), 617–625 (1993)

    Article  Google Scholar 

  27. J. Lian, Z. Yang, W. Sun, Y. Guo, L. Zheng, J. Li, B. Shi, Y. Ma, An image segmentation method of a modified SPCNN based on human visual system in medical images. Neurocomputing 333, 292–306 (2019)

    Article  Google Scholar 

  28. C. Liu, W. Liu, W. Xing, A weighted edge-based level set method based on multi-local statistical information for nocoisy image segmentation. J. Visual Commun. Image Represent. 59, 89–107 (2019)

    Article  Google Scholar 

  29. A.G. Mathioudakis, M. Salakari, L. Pylkkanen, Z. Saz-Parkinson, A. Bramesfeld, S. Deandrea, D. Lerda, L. Neamtiu, H. Pardo-Hernandez, I. Solà et al., Systematic review on women’s values and preferences concerning breast cancer screening and diagnostic services. Psycho-Oncology (2019)

    Google Scholar 

  30. H. Min, S.S. Chandra, S. Crozier, A.P. Bradley, Multi-scale sifting for mammographic mass detection and segmentation. Biomed. Phys. Eng. Exp. (2019)

    Google Scholar 

  31. B. Mughal, N. Muhammad, M. Sharif, Adaptive hysteresis thresholding segmentation technique for localizing the breast masses in the curve stitching domain. Int. J. Med. Inform. 126, 26–34 (2019)

    Article  Google Scholar 

  32. T. Nayak, N. Bhat, V. Bhat, S. Shetty, M. Javed, P. Nagabhushan, Automatic segmentation and breast density estimation for cancer detection using an efficient watershed algorithm, in Data Analytics and Learning (Springer, 2019), pp. 347–358

    Google Scholar 

  33. O.P.S. Neto, O. Carvalho, W. Sampaio, A. Corrêa, A. Paiva, Automatic segmentation of masses in digital mammograms using particle swarm optimization and graph clustering, in 2015 International Conference on Systems, Signals and Image Processing (IWSSIP) (IEEE, 2015), pp. 109–112

    Google Scholar 

  34. D. Oliva, M.A. Elaziz, S. Hinojosa, Image segmentation using Kapur’s entropy and a hybrid optimization algorithm, in Metaheuristic Algorithms for Image Segmentation: Theory and Applications (Springer, 2019), pp. 85–99

    Google Scholar 

  35. D. Oliva, M.A. Elaziz, S. Hinojosa, Multilevel thresholding for image segmentation based on metaheuristic algorithms, in Metaheuristic Algorithms for Image Segmentation: Theory and Applications (Springer, 2019), pp. 59–69

    Google Scholar 

  36. D. Oliva, S. Hinojosa, V. Osuna-Enciso, E. Cuevas, M. Pérez-Cisneros, G. Sanchez-Ante, Image segmentation by minimum cross entropy using evolutionary methods. Soft Comput. 1–20 (2017)

    Google Scholar 

  37. G. Pei, Y. Zhang, Digital Orthopedics (Springer, 2019)

    Google Scholar 

  38. M. Posso, J. Louro, M. Sánchez, M. Román, C. Vidal, M. Sala, M. Baré, X. Castells, Study group B, et al., Mammographic breast density: how it affects performance indicators in screening programmes? Eur. J. Radiol. 110, 81–87 (2019)

    Google Scholar 

  39. T. Sadad, A. Munir, T. Saba, A. Hussain, Fuzzy c-means and region growing based classification of tumor from mammograms using hybrid texture feature. J. Comput. Sci. 29, 34–45 (2018)

    Article  MathSciNet  Google Scholar 

  40. W.B. de Sampaio, A.C. Silva, A.C. de Paiva, M. Gattass, Detection of masses in mammograms with adaption to breast density using genetic algorithm, phylogenetic trees, LBP and SVM. Exp. Syst. Appl. 42(22), 8911–8928 (2015)

    Article  Google Scholar 

  41. E. Seeram, Digital image processing concepts, in Digital Radiography (Springer, 2019), pp. 21–39

    Google Scholar 

  42. P. Shi, J. Zhong, A. Rampun, H. Wang, A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms. Comput. Biol. Med. 96, 178–188 (2018)

    Article  Google Scholar 

  43. R. Storn, K. Price, Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Opt. 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  44. N. Thome, S. Bernard, V. Bismuth, F. Patoureaux, et al., Multitask classification and segmentation for cancer diagnosis in mammography (2019)

    Google Scholar 

  45. G. Valvano, G. Santini, N. Martini, A. Ripoli, C. Iacconi, D. Chiappino, D. Della Latta, Convolutional neural networks for the segmentation of microcalcification in mammography imaging. J. Healthc. Eng. (2019)

    Google Scholar 

  46. X.S. Yang, Firefly algorithms for multimodal optimization, in International Symposium on Stochastic Algorithms (Springer, 2009), pp. 169–178

    Google Scholar 

  47. P.Y. Yin, Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl. Math. Comput. 184(2), 503–513 (2007)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad de Guadalajara, CUCEI, Blvd. Gral. Marcelino García Barragán 1421, Olí­mpica, 44430, Guadalajara, Jalisco, Mexico

    Erick Rodrí­guez-Esparza & Daniel Zaldivar

  2. University of Lorraine, LORIA, Campus Scientifique, 615 Rue du Jardin-Botanique, 54506, Vandœuvre-lès-Nancy, Lorraine, France

    Laura A. Zanella-Calzada

  3. Universidad Autónoma de Zacatecas “Francisco García Salinas”, Jardín Juárez 147, Centro, 98000, Zacatecas, Zacatecas, Mexico

    Carlos E. Galván-Tejada

Authors
  1. Erick Rodrí­guez-Esparza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laura A. Zanella-Calzada
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Zaldivar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carlos E. Galván-Tejada
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Erick Rodrí­guez-Esparza or Laura A. Zanella-Calzada .

Editor information

Editors and Affiliations

  1. CUCEI, University of Guadalajara, Guadajalara, Mexico

    Diego Oliva

  2. CUCEI, University of Guadalajara, Guadajalara, Mexico

    Salvador Hinojosa

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rodrí­guez-Esparza, E., Zanella-Calzada, L.A., Zaldivar, D., Galván-Tejada, C.E. (2020). Automatic Detection of Malignant Masses in Digital Mammograms Based on a MCET-HHO Approach. In: Oliva, D., Hinojosa, S. (eds) Applications of Hybrid Metaheuristic Algorithms for Image Processing. Studies in Computational Intelligence, vol 890. Springer, Cham. https://doi.org/10.1007/978-3-030-40977-7_15

Download citation

Publish with us

Policies and ethics

Search

Navigation