State-of-the-Art Techniques for Mammogram Enhancement: A Comprehensive Discussion of Emerging Research Gaps and Remedial Solution

  • Vikrant BhatejaEmail author
  • Mukul Misra
  • Shabana Urooj
Part of the Studies in Computational Intelligence book series (SCI, volume 861)


Breast cancer may be predicted via variety of appearances on mammograms: from the obvious benign or malignant masses, to density variations, subtle asymmetries, to rarely visible and obscured calcifications.


  1. S.A. Ahmad, M.N. Taib, N.E.A. Khalid, H. Taib, An analysis of image enhancement techniques for dental X-ray image interpretation. Int. J. Mach. Learn. Comput. 2(3), 292–297 (2012)Google Scholar
  2. S. Anand, S. Gayathri, Mammogram image enhancement by two-stage adaptive histogram equalization. Optik 126(21), 3150–3152 (2015)CrossRefGoogle Scholar
  3. S. Anand, R.S. Kumari, S. Jeeva, T. Thivya, Directionlet transform based sharpening and enhancement of mammographic X-ray images. Biomed. Signal Process. Control 8(4), 391–399 (2013)CrossRefGoogle Scholar
  4. M. Basu, Gaussian based edge-detection methods – a survey. IEEE Trans. Syst. Man Cybern—Part C: Appl. Rev. 32, 252–260 (2002)Google Scholar
  5. G.G. Bhutada, R.S. Anand, S.C. Saxena, Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform. Digit. Signal Process. 21(1), 118–130 (2011)CrossRefGoogle Scholar
  6. L.M. Bruce, R.R. Adhami, Classifying mammographic mass shapes using the wavelet transform modulus-maxima method. IEEE Trans. Med. Imaging 18(12), 1170–1177 (2009)CrossRefGoogle Scholar
  7. C.B. Caldwell, S.J. Stapleton, D.W. Holdsworth, R.A. Jong, W.J. Weiser, G. Cooke, M.J. Yaffe, Characterisation of mammographic parenchymal pattern by fractal dimension. Phys. Med. Biol. 35(2), 235–247 (1990)CrossRefGoogle Scholar
  8. C. Chang, A.F. Laine, Coherence of multiscale features for enhancement of digital mammograms. IEEE Trans. Inf. Technol. Biomed. 3(1) 32–46 (1999)Google Scholar
  9. G. Chen, K. Panetta, S. Agaian, New edge detection algorithms using alpha weighted quadratic filter, in Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC-2011), Alaska, USA, October 2011, pp. 3167–3172Google Scholar
  10. H.D. Cheng, H. Xu, A novel fuzzy logic approach to mammogram contrast enhancement. Inf. Sci. 148(4), 167–184 (2002)zbMATHCrossRefGoogle Scholar
  11. H.D. Cheng, X. Cai, X. Chen, L. Hu, X. Lou, Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognit. 36(12), 2967–2991 (2003)zbMATHCrossRefGoogle Scholar
  12. S. Chiandussi, G. Ramponi, Nonlinear unsharp masking for the enhancement of document images, in Proceedings of IEEE 8th European Signal Processing Conference, (EUSIPCO-1996), Trieste, Italy, September 1996, pp. 1–4Google Scholar
  13. H. Deng, X. Sun, M. Liu, C. Ye, X. Zhou, Image enhancement based on intuitionistic fuzzy sets theory. IET Image Process. 10(10), 701–709 (2016)CrossRefGoogle Scholar
  14. A.P. Dhawan, G. Buelloni, R. Gordon, Enhancement of mammographic features by optimal adaptive neighborhood image processing. IEEE Trans. Med. Imaging 5(1), 8–15 (1986)CrossRefGoogle Scholar
  15. S. Dippel, M. Stahl, R. Wiemker, T. Blaffert, Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform. IEEE Trans. Med. Imaging 21(4), 343–353 (2002)CrossRefGoogle Scholar
  16. L.H. Eadie, P. Taylor, A.P. Gibson, A systematic review of computer-assisted diagnosis in diagnostic cancer imaging. Eur. J. Radiol. 81(1), e70–e76 (2012)CrossRefGoogle Scholar
  17. T.L. Economopoulos, P.A. Asvestas, G.K. Matsopoulos, Contrast enhancement of images using partitioned iterated function systems. Image Vis. Comput. 28(1), 45–54 (2010)CrossRefGoogle Scholar
  18. M.M. Eltoukhy, I. Faye, B.B. Samir, A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram. Comput. Biol. Med. 40(4), 384–391 (2010)Google Scholar
  19. K. Ganesan, U.R. Acharya, C.K. Chua, L.C. Min, K.T. Abraham, K.-H. Ng, Computer-aided breast cancer detection using mammograms: a review. IEEE Rev. Biomed. Eng. 6, 77–98 (2013)CrossRefGoogle Scholar
  20. R.C. Gonzalez, R.E. Woods, Digital Image Processing, 3rd edn. (Prentice Hall, USA, 2007)Google Scholar
  21. R. Gordon, R.M. Rangayyan, Feature enhancement of film mammograms using fixed and adaptive neighborhoods. Appl. Opt. 23(4), 560–564 (1984)CrossRefGoogle Scholar
  22. M.A. Guerroudji, Z. Ameur, A new approach for the detection of mammary calcifications by using the white top-hat transform and thresholding of Otsu. Optik 127(3), 1251–1259 (2016)CrossRefGoogle Scholar
  23. Y.N. Guo, M. Dong, Z. Yang, X. Gao, K. Wang, C. Luo, Y. Ma, J. Zhang, A new method of detecting micro-calcification clusters in mammograms using Contourlet transform and non-linking simplified PCNN. Comput. Methods Progr. Biomed. 130, 31–45 (2016)CrossRefGoogle Scholar
  24. V.S. Hari, R.V.P. Jagathy, R. Gopikakumari, Enhancement of calcifications in mammograms using Volterra series based quadratic filter, in Proceedings of IEEE International Conference on Data Science & Engineering (ICDSE-2012), Cochin, Kerala, India, July 2012, pp. 85–89Google Scholar
  25. V.S. Hari, R.V.P. Jagathy, R. Gopikakumari, Unsharp masking using quadratic filter for the enhancement of fingerprints in noisy background. Pattern Recognit. 46(12), 3198–3207 (2013)CrossRefGoogle Scholar
  26. W. He, A. Juette, E.R.E. Denton, A. Oliver, R. Martí, R. Zwiggelaar, A review on automatic mammographic density and parenchymal segmentation. Int. J. Breast Cancer 2015, 1–31 (2015), Article ID 276217CrossRefGoogle Scholar
  27. P. Heinlein, J. Drexl, W. Schneider, Integrated wavelets for enhancement of micro-calcifications in digital mammography. IEEE Trans. Med. Imaging 22(3), 402–413 (2003)CrossRefGoogle Scholar
  28. K. Hu, X. Gao, F. Li, Detection of suspicious lesions by adaptive thresholding based on multiresolution analysis in mammograms. IEEE Trans. Instrum. Meas. 60(2), 462–472 (2011)CrossRefGoogle Scholar
  29. S. Jenifer, S. Parasuraman, A. Kadirvelu, Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm. Appl. Soft Comput. 42, 167–177 (2016)CrossRefGoogle Scholar
  30. J. Jiang, B. Yao, A.M. Wason, Integration of fuzzy logic and structure tensor towards mammogram contrast enhancement. Comput. Med. Imaging Graph. 29(1), 83–90 (2005)CrossRefGoogle Scholar
  31. L. Kanelovitch, Y. Itzchak, A. Rundstein, M. Sklair, H. Spitzer, Biologically derived companding algorithm for high dynamic range mammography images. IEEE Trans. Biomed. Eng. 60(8), 2253–2261 (2013)CrossRefGoogle Scholar
  32. J.K. Kim, J.M. Park, K.S. Song, H.W. Park, Adaptive mammographic image enhancement using first derivative and local statistics. IEEE Trans. Med. Imaging 16(5), 495–502 (1997)CrossRefGoogle Scholar
  33. G. Kom, A. Tiedeu, M. Kom, Automated detection of masses in mammograms by local adaptive thresholding. Comput. Biol. Med. 37(1), 37–48 (2007)CrossRefGoogle Scholar
  34. P. Kuş, İ. Karagöz, Detection of micro-calcification clusters in digitized X-ray mammograms using unsharp masking and image statistics. Turk. J. Electr. Eng. Comput. Sci. 21(1), 2048–2061 (2013)CrossRefGoogle Scholar
  35. A.F. Laine, J. Fan, S. Schuler, A framework for contrast enhancement by dyadic wavelet analysis. Digit. Mammogr. 91–100 (1994)Google Scholar
  36. Y.H. Lee, S.Y. Park, A study of convex/concave edges and edge-enhancing operators based on the Laplacian. IEEE Trans. Circuits Syst. 37(7), 940–946 (1990)MathSciNetCrossRefGoogle Scholar
  37. H. Li, R. Liu, S. Lo, Fractal modelling and segmentation for the enhancement of microcalcifications in digital mammograms. IEEE Trans. Med. Imaging 16(6), 785–798 (1997)CrossRefGoogle Scholar
  38. H. Li, Y. Wang, K.J.R. Liu, S.B. Lo, M.T. Freedman, Computerized radiographic mass detection—I: lesion site selection by morphological enhancement and contextual segmentation. IEEE Trans. Med. Imaging 20(4), 289–301 (2001)CrossRefGoogle Scholar
  39. V.J. Mathews, Adaptive polynomial filters. IEEE Signal Process. Mag. 8(3), 10–26 (1991)CrossRefGoogle Scholar
  40. A. Mencattini, M. Salmeri, R. Lojacono, M. Frigerio, F. Caselli, Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. IEEE Trans. Instrum. Meas. 57(7), 1422–1430 (2008)CrossRefGoogle Scholar
  41. S.K. Mitra, H. Li, I. Li, T.-H. Yu, A new class of non-linear filters for image enhancement, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, (ICASSP-1991), Toronto, Canada, April 1991, pp. 2525–2528Google Scholar
  42. H. Mohamed, M.S. Mabrouk, A. Sharawy, Computer aided detection system for micro calcifications in digital mammograms. Comput. Methods Progr. Biomed. 116(3), 226–235 (2014)CrossRefGoogle Scholar
  43. Mohanalin, P.K. Kalra, N. Kumar, An automatic method to enhance microcalcifications using normalized Tsallis entropy. Signal Process. 90(3), 952–958 (2010)zbMATHCrossRefGoogle Scholar
  44. A.K. Mohideen, K. Thangavel, Region-based contrast enhancement of digital mammograms using an improved watershed segmentation. Int. J. Image Graph. 13(1), 1–25 (2013)MathSciNetCrossRefGoogle Scholar
  45. W.M. Morrow, R.B. Paranjape, R.M. Rangayyan, J.E.L. Desautels, Region-based contrast enhancement of mammograms. IEEE Trans. Med. Imaging 11(3), 392–406 (1992)CrossRefGoogle Scholar
  46. S. Nercessian, K. Panetta, S. Agaian, Non-linear multi-scale image enhancement using the luminance and contrast masking characteristics of the human visual system. IEEE Trans. Image Process. 22(9), 3549–3561 (2013)Google Scholar
  47. F. Pak, H.R. Kanan, A. Alikhassi, Breast cancer detection and classification in digital mammography based on NSCT and super resolution. Comput. Methods Progr. Biomed. 122(2), 89–107 (2015)Google Scholar
  48. K.A. Panetta, Z. Yicong, S.S. Agaian, H. Jia, Non-linear unsharp masking for mammogram enhancement. IEEE Trans. Inf. Technol. Biomed. 15(6), 918–928 (2011)CrossRefGoogle Scholar
  49. A. Papadopoulos, D.I. Fotiadis, L. Costaridou, Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. Comput. Biol. Med. 38(10), 1045–1055 (2008)CrossRefGoogle Scholar
  50. N. Petrick, H.-P. Chan, B. Sahiner, D. Wei, An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection. IEEE Trans. Med. Imaging 15(1), 59–67 (1996)CrossRefGoogle Scholar
  51. E.D. Pisano, S. Zong, B.M. Hemminger, M. Deluca, R.E. Johnston, K. Muller, M.P. Braeuning, S.M. Pizer, Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J. Digit. Imaging 11(4), 193–200 (1998)CrossRefGoogle Scholar
  52. I. Pitas, A.N. Venetsanopoulos, Morphological shape decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 38–45 (1990)CrossRefGoogle Scholar
  53. I. Pitas, A.N. Venetsanopoulos, Order statistics in digital image processing. Proc. IEEE 80(12), 1893–1921 (1992)CrossRefGoogle Scholar
  54. S.M. Pizer, E.P. Amburn, J.D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. Haar Romeny, J.B. Zimmerman, K. Zuiderveld, Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)CrossRefGoogle Scholar
  55. A. Polesel, G. Ramponi, V.J. Mathews, Image enhancement via adaptive unsharp masking. IEEE Trans. Image Process. 9(3), 505–510 (2000)CrossRefGoogle Scholar
  56. G. Ramponi, Bi-impulse response design of isotropic quadratic filters. Proc. IEEE 78(4), 665–667 (1990)CrossRefGoogle Scholar
  57. G. Ramponi, A cubic unsharp masking technique for contrast enhancement. Signal Process. 67(2), 211–222 (1998)zbMATHCrossRefGoogle Scholar
  58. G. Ramponi, A. Polesel, Rational unsharp masking technique. J. Electron. Imaging 7(2), 333–338 (1998)zbMATHCrossRefGoogle Scholar
  59. G. Ramponi, G.L. Sicuranza, Quadratic digital filters for image processing. IEEE Trans. Acoust. Speech Signal Process. 36(6), 937–939 (1988)zbMATHCrossRefGoogle Scholar
  60. G. Ramponi, G.L. Sicuranza, Image sharpening using a polynomial operator, in Proceedings of IEEE European Conference on Circuit Theory and Design (ECCTD-1993), Davos, Switzerland, September 1993, pp. 1431–1436Google Scholar
  61. R.M. Rangayyan, L. Shen, Y. Shen, J.E.L. Desautels, H. Bryant, T.J. Terry, N. Horeczko, M.S. Rose, Improvement of sensitivity of breast cancer diagnosis with adaptive neighborhood contrast enhancement of mammograms. IEEE Trans. Inf. Technol. Biomed. 1(3), 161–170 (1997)CrossRefGoogle Scholar
  62. J. Rogowska, K. Preston, D. Sashin, Evaluation of digital unsharp masking and local contrast stretching as applied to chest radiology. IEEE Trans. Biomed. Eng. 35(10), 817–827 (1988)CrossRefGoogle Scholar
  63. P. Sakellaropoulos, L. Costaridou, A wavelet-based spatially adaptive method for mammographic contrast enhancement. Phys. Med. Biol. 48(6), 787–803 (2003)CrossRefGoogle Scholar
  64. D. Sankar, T. Thomas, A new fast fractal modelling approach for the detection of microcalcifications in mammograms. J. Digit. Imaging 23(5), 538–546 (2009)CrossRefGoogle Scholar
  65. G.L. Sicuranza, Quadratic filters for signal processing. Proc. IEEE 80(8), 1263–1285 (1992)CrossRefGoogle Scholar
  66. R. Sivaramakrishna, N.A. Obuchowski, W.A. Chilcote, G. Cardenosa, K.A. Powell, Comparing the performance of mammographic enhancement algorithms. Am. J. Roentgenol. 175(1), 45–51 (2000)CrossRefGoogle Scholar
  67. J.A. Stark, Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans. Image Process. 9(5), 889–896 (2000)CrossRefGoogle Scholar
  68. T. Stojic, I. Reljin, B. Reljin, Local contrast enhancement in digital mammography by using mathematical morphology, in Proceedings of IEEE International Symposium Signals, Circuits and Systems (ISSCS-2005), Romania, July 2005, vol. 2, pp. 609–612Google Scholar
  69. M. Sundaram, K. Ramar, N. Arumugam, G. Prabin, Histogram modified local contrast enhancement for mammogram images. Appl. Soft Comput. 11(8), 5809–5816 (2011)CrossRefGoogle Scholar
  70. J. Tang, X. Liu, Q. Sun, A direct image contrast enhancement algorithm in the wavelet domain for screening mammograms. IEEE J. Sel. Top. Signal Process. 3(1), 74–80 (2009)CrossRefGoogle Scholar
  71. S. Thurnhofer, S.K. Mitra, A general framework for quadratic Volterra filters for edge enhancement. IEEE Trans. Image Process. 5(6), 950–963 (1996)CrossRefGoogle Scholar
  72. T.C. Wang, N.B. Karayiannis, Detection of micro-calcifications in digital mammograms using wavelets. IEEE Trans. Med. Imaging 17(4), 498–509 (1998)CrossRefGoogle Scholar
  73. Z. Wu, J. Yuan, B. Lv, X. Zheng, Digital mammography image enhancement using improved unsharp masking approach, in Proceedings of IEEE 3rd International Congress on Image and Signal Processing, Yantai, China, June 2010, pp. 668–671Google Scholar
  74. S. Wu, S. Yu, Y. Yang, Y. Xie, Feature and contrast enhancement of mammographic image based on multiscale analysis and morphology. Comput. Math. Methods Med. 2013(10), 1–8 (2013)Google Scholar
  75. Y. Yang, Z. Su, L. Sun, Medical image enhancement algorithm based on wavelet transform. Electron. Lett. 46(2), 120–121 (2010)CrossRefGoogle Scholar
  76. J.H. Yoon, Y.M. Ro, Enhancement of the contrast in mammographic images using the homomorphic filter method. IEICE Trans. Inf. Syst. 85(1), 298–303 (2002)Google Scholar
  77. Y. Zhou, K.A. Panetta, S.S. Agaian, Mammogram enhancement using alpha weighted quadratic filter, in Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, Minnesota, September 2009, pp. 3681–3684Google Scholar
  78. J.B. Zimmerman, S.M. Pizer, E.V. Staab, J.R. Perry, W. Mccartney, B.C. Brenton, An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement. IEEE Trans. Med. Imaging 7(4), 304–312 (1988)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringShri Ramswaroop Memorial Group of Professional Colleges (SRMGPC)LucknowIndia
  2. 2.Dr. A.P.J. Abdul Kalam Technical UniversityLucknowIndia
  3. 3.Faculty of Electronics and Communication EngineeringShri Ramswaroop Memorial University (SRMU)BarabankiIndia
  4. 4.Department of Electrical Engineering, College of EngineeringPrincess Nourah Bint Abdulrahman UniversityRiyadhKingdom of Saudi Arabia

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