Multimedia Tools and Applications

, Volume 78, Issue 10, pp 13005–13031 | Cite as

Breast cancer detection in mammography using spatial diversity, geostatistics, and concave geometry

  • Geraldo Braz JuniorEmail author
  • Simara V. da Rocha
  • João D. S. de Almeida
  • Anselmo C. de Paiva
  • Aristófanes C. Silva
  • Marcelo Gattass


Breast cancer is a global health problem which mainly affects the female population. It is known that early detection increases the chances of effective treatment, improving the disease prognosis. It remains a challenge to detect the lesion with high detection rate and ensure, at the same time, low rates of false positives . Aiming this objective, this work proposes an efficient method for detection of mass regions on digitized mammograms though diversity analysis, geostatistical and concave geometry (Alpha Shapes). We evaluate the detection rate for each feature extraction using Support Vector Machine in MIAS and DDSM database, with 74 and 621 mammograms, respectively, all containing at least one mass region. The obtained results are promising, reaching 97.30% of detection rate and 0.89 false positive per image for MIAS database and also 91.63% of detection rate and 0.86 false positive per image for DDSM database. Specifically, in DDSM obtaining high detection rate and low rate of false positives when using concave geometry to extract features in a large database.


Mammography Detection False positive reduction Diversity analysis Geostatistical analysis Concave geometry Alpha-shapes 



The authors thank CNPq and FAPEMA for the financial support.


  1. 1.
    American Cancer Society A (2013) Learn about breast cancerGoogle Scholar
  2. 2.
    Anitha J, Peter JD, Pandian SIA (2017) A dual stage adaptive thresholding (dusat) for automatic mass detection in mammograms. Comput Methods Programs Biomed 138:93–104Google Scholar
  3. 3.
    Anselin L (2001) Computing enviroments for spatial data analysis. J Geogr Syst 2:201–220Google Scholar
  4. 4.
    Basheer NM, Mohammed MH (2013) Segmentation of breast masses in digital mammograms using adaptive median filtering and texture analysis. Int J Recent Technol Eng(IJRTE) 2(1):39–43MathSciNetGoogle Scholar
  5. 5.
    Bird R, Wallace T, Yankaskas B (1992) Analysis of cancers missed at screening mammography. Radiology 184(3):613–617Google Scholar
  6. 6.
    Braz JG, de Paiva CA, Corrêa Silva A, Cesar Muniz de Oliveira A (2009) Classification of breast tissues using moran’s index and geary’s coefficient as texture signatures and svm. Comput Biol Med 39(12):1063–1072Google Scholar
  7. 7.
    Braz JG, da Rocha SV, Gattass M, Silva AC, de Paiva AC (2013) A mass classification using spatial diversity approaches in mammography images for false positive reduction. Expert Syst Appl 40(18):7534–7543Google Scholar
  8. 8.
    Buzas M, Hayek L (1998) She analysis for biofacies identification. J Foraminiferal Res 28(3):233–239Google Scholar
  9. 9.
    Camargo J (1993) Must dominance increase with the number of subordinate species in competitive interactions. J Theor Biol 161(4):537–542Google Scholar
  10. 10.
    Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1(6):679–698Google Scholar
  11. 11.
    Cheng Y (1995) Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Mach Intell 17(8):790–799Google Scholar
  12. 12.
    Dhungel N, Carneiro G, Bradley AP (2015) Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 International Conference on digital image computing: techniques and applications (DICTA). IEEE, pp 1–8Google Scholar
  13. 13.
    Ding J, Kuo C, Hong W (2009) An efficient image segmentation technique by fast scanning and adaptive merging. Graphical Models and Image ProcessingGoogle Scholar
  14. 14.
    Gao X, Wang Y, Li X, Tao D (2010) On combining morphological component analysis and concentric morphology model for mammographic mass detection. IEEE Trans Inform Technol Biomed 14(2):266–273Google Scholar
  15. 15.
    Gonzalez R, Woods R (2010) Processamento Digital de Imagens, 3rd edn. Pearson Prentice Hall, São PauloGoogle Scholar
  16. 16.
    Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 1(6):610–621Google Scholar
  17. 17.
    Heath M, Bowyer KDK (1998) Current status of the digital database for screening mammography. Digit Mammograph 1:457–460Google Scholar
  18. 18.
    Hong BW, Sohn BS (2010) Segmentation of regions of interest in mammograms in a topographic approach. IEEE Trans Inform Technol Biomed 14(1):129–139Google Scholar
  19. 19.
    Jost L (2010) The relation between evenness and diversity. Diversity 2(2):207–232Google Scholar
  20. 20.
    Kashyap KL, Bajpai MK, Khanna P (2017) An efficient algorithm for mass detection and shape analysis of different masses present in digital mammograms. Multimed Tools Appl, 1–21Google Scholar
  21. 21.
    Ke L, Mu N, Kang Y (2010) Mass computer-aided diagnosis method in mammogram based on texture features. In: 3rd International conference on biomedical engineering and informatics (BMEI), vol 1. IEEE, Yantai, pp 354–357Google Scholar
  22. 22.
    Levine N (1996) Análise Estatística de Dados Geográficos Editora Unsep. São Paulo, BrasilGoogle Scholar
  23. 23.
    Liu X, Xu X, Liu J, Feng Z (2011) A new automatic method for mass detection in mammography with false positives reduction by supported vector machine. In: 4th International Conference on biomedical engineering and informatics, vol 1. IEEE, Shangai, pp 33–37, DOI
  24. 24.
    Lladó X, Oliver A, Freixenet J, Martí R, Martí J (2009) A textural approach for mass false positive reduction in mammography. Comput Med Imag Graph 33(6):415–422Google Scholar
  25. 25.
    MacQueen J et al. (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 14. University of California Berkeley, California, pp 281–297Google Scholar
  26. 26.
    Magurran AE (2004) Measuring biological diversity. Taylor & FrancisGoogle Scholar
  27. 27.
    May R (1975) Patterns of species abundance and diversity. Ecol Evol Commun, 81–120Google Scholar
  28. 28.
    Moayedi F, Azimifar Z, Boostani R, Katebi S (2010) Contourlet-based mammography mass classification using the svm family. Comput Biol Med 40(4):373–383Google Scholar
  29. 29.
    Montero RS, Bribiesca E (2009) State of the art of compactness and circularity measures. Int Math Forum 4(25–28):1305–1335MathSciNetzbMATHGoogle Scholar
  30. 30.
    Mucke HE (1994) Three-dimensional alpha shapes. ACM Trans Graph 13:43–72zbMATHGoogle Scholar
  31. 31.
    Obenauer S (2008) Bi-rads, lexicon. In: Encyclopedia of diagnostic imaging. Springer, pp 131–134Google Scholar
  32. 32.
    Oliver A, Lladó X, Freixenet J, Martí R, Pérez E, Pont J, Zwiggelaar R (2010) Influence of using manual or automatic breast density information in a mass detection cad system. Acad Radiol 17(7):877–883Google Scholar
  33. 33.
    Pielou E (1975) Ecological diversity. Wiley, New YorkGoogle Scholar
  34. 34.
    Pizer SM (1987) Adaptive histogram equalization and its variotions. Comput Vis Graph Image Process, 355–368Google Scholar
  35. 35.
    Rahmati P, Adler A, Hamarneh G (2012) Mammography segmentation with maximum likelihood active contours. Medical Image AnalysisGoogle Scholar
  36. 36.
    Ramos R, Nascimento M, Pereira D (2012) Texture extraction: an evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms. Expert Systems with ApplicationsGoogle Scholar
  37. 37.
    Ripley BD (1977) Modelling spatial patterns. J Roy Statist Soc, 172–212Google Scholar
  38. 38.
    Sahba F, Venetsanopoulos A (2010) Mean shift based algorithm for mammographic breast mass detection. In: 17th IEEE International conference on image processing (ICIP). IEEE, Hong Kong, pp 3629–3632Google Scholar
  39. 39.
    Sai Deepak K, Kartheek Medathati N, Sivaswamy J (2012) Detection and discrimination of disease related abnormalities based on learning normal cases. Pattern Recogn 45:3707–3716Google Scholar
  40. 40.
    Sampaio W, Diniz EM, Silva AC, Paiva AC, Gatass M (2011) Detection of masses in mammogram images using cnn, geostatistic functions and svm. Comput Biol Med 41:653–664Google Scholar
  41. 41.
    Shannon C (2001) A mathematical theory of communication. ACM SIGMOBILE Mob Comput Commun Rev 5(1):3–55MathSciNetGoogle Scholar
  42. 42.
    Silva Neto OP, Silva AC, Paiva AC, Gattass M (2017) Automatic mass detection in mammography images using particle swarm optimization and functional diversity indexes. Multimed Tools Appl, 1–27Google Scholar
  43. 43.
    Simpson E (1949) Measurement of diversity. Nature; NatureGoogle Scholar
  44. 44.
    Sousa J R F d S, Silva AC, de Paiva AC, Nunes RA (2010) Methodology for automatic detection of lung nodules in computerized tomography images computer methods and programs. Biomedicine 98(1): 1–14Google Scholar
  45. 45.
    Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, Ricketts I, Stamatakis E, Cerneaz N, Kok S et al (1994) The mammographic images analysis society digital mammogram database. Exerpta Medica Int Congress Series 1069:375–378Google Scholar
  46. 46.
    Tai SC, Chen ZS, Tsai WT (2014) An automatic mass detection system in mammograms based on complex texture features. IEEE J Biomed Health Inf 18 (2):618–627Google Scholar
  47. 47.
    Terada T, Fukumizu Y, Yamauchi H, Chou H, Kurumi Y (2010) Detecting mass and its region in mammograms using mean shift segmentation and iris filter. In: International Symposium on communications and information technologies (ISCIT). IEEE, Tokyo, pp 1176–1179Google Scholar
  48. 48.
    Tzikopoulos S, Mavroforakis M, Georgiou H, Dimitropoulos N, Theodoridis S (2011) A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry. Comput Methods Programs Biomed 102 (1):47–63Google Scholar
  49. 49.
    Vapnik V (1998) Statistical learning theory. Wiley, New YorkzbMATHGoogle Scholar
  50. 50.
    Vikhe P, Thool V (2016) Mass detection in mammographic images using wavelet processing and adaptive threshold technique. J Med Syst 40(4):82Google Scholar
  51. 51.
    Wang X, Li L, Xu W, Liu W, Lederman D, Zheng B (2012) Improving performance of computer-aided detection of masses by incorporating bilateral mammographic density asymmetry: an assessment. Acad Radiol 19(3):303–310Google Scholar
  52. 52.
    Wei J, Chan H, Zhou C, Wu Y, Sahiner B, Hadjiiski L, Roubidoux M, Helvie M (2011) Computer-aided detection of breast masses: four-view strategy for screening mammography. Med Phys 38(4):1867– 1876Google Scholar
  53. 53.
    Wei J, Chan HP, Zhou C, Wu YT, Sahiner B, Hadjiiski LM, Roubidoux MA, Helvie MA (2011) Computer-aided detection of breast masses: four-view strategy for screening mammography. Med Phys 38:1867Google Scholar
  54. 54.
    Zheng Y (2010) Breast cancer detection with gabor features from digital mammograms. Algorithms 3:44–62zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Federal University of MaranhaoComputer Applied Group - NCASão LuisBrazil
  2. 2.Tecgraf - Group of Computer Graphics Technology - Catholic University of Rio de JaneiroRio de JaneiroBrazil

Personalised recommendations