Region-Based and Feature Based Mammogram Enhancement Techniques

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


Region-based enhancement algorithms operate adaptively based on the availability of features and enhances them with respect to their background (irrespective of its shape or size). Region-based approach defines an adaptive region for processing (about a pixel); whose size is dependent upon the availability of features within that region (Pratt et al. in Image enhancement. PIKS Scientific Inside, pp. 247–305, 2001). In such a case, contrast manipulation algorithms can be then applied on a region rather than pixel basis.


  1. S. Anand, R.S. Kumari, S. Jeeva, T. Thivya, Directionlet transform based sharpening and enhancement of mammographic X-ray images. Biomedical Sign. Process. Control 8(4), 391–399 (2013)CrossRefGoogle Scholar
  2. A.C. Bovik, Handbook of Image and Video Processing, 2nd edn. (Elsevier Academic Press, Amsterdam, 2010)zbMATHGoogle Scholar
  3. 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
  4. G.G. Bhutada, R.S. Anand, S.C. Saxena, Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform. Digital Sign. Process. 21(1), 118–130 (2011)CrossRefGoogle Scholar
  5. C. Chang, A.F. Laine, Coherence of multiscale features for enhancement of digital mammograms. IEEE Trans. Inf. Technol. Biomed. 3(1), 32–46 (1999)CrossRefGoogle Scholar
  6. 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. Phy. Medicine Biol. 35(2), 235–247 (1990)CrossRefGoogle Scholar
  7. 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
  8. 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)CrossRefGoogle Scholar
  9. R.C. Gonzalez, R.E. Woods, Digital Image Processing, 3rd edn. (Prentice Hall, USA, 2007)Google Scholar
  10. 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 Programs Biomed. 130, 31–45 (2016)CrossRefGoogle Scholar
  11. 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
  12. K. Hu, X. Gao, F. Li, Detection of suspicious lesions by adaptive thresholding based on multire solution analysis in mammograms. IEEE Trans. Instrum. Meas. 60(2), 462–472 (2011)CrossRefGoogle Scholar
  13. A.F. Laine, J. Fan, S. Schuler, A framework for contrast enhancement by dyadic wavelet analysis. Digit. Mammography 91–100, July (1994)Google Scholar
  14. 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
  15. 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
  16. 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
  17. 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
  18. F. Pak, H.R. Kanan, A. Alikhassi, Breast cancer detection and classification in digital mammography based on NSCT and super resolution. Comput. Methods Programs Biomed. 122(2), 89–107 (2015)CrossRefGoogle Scholar
  19. W.K. Pratt, Image Enhancement. Digital Image Processing, 4th edn. (PIKS Scientific Inside, 2001), pp. 247–305Google Scholar
  20. P. Sakellaropoulos, L. Costaridou, A wavelet-based spatially adaptive method for mammographic contrast enhancement. Phys. Med. Biol. 48(6), 787–803 (2003)CrossRefGoogle Scholar
  21. 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
  22. J. Tang, E. Peli, S. Acton, Image enhancement using a contrast measure in the compressed domain. IEEE Sign. Process. Lett. 10(10), 289–292 (2003)CrossRefGoogle Scholar
  23. J. Tang, X. Liu, Q. Sun, A direct image contrast enhancement algorithm in the wavelet domain for screening mammograms. IEEE J. Sel. Top. Sign. Process. 3(1), 74–80 (2009)CrossRefGoogle Scholar
  24. 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
  25. S. Wu, S. Yu, Y. Yang, Y. Xie, Feature and contrast enhancement of mammographic image based on multiscale analysis and morphology. Comput. Math. Meth. Med. 2013(10), 1–8 (2013)MathSciNetzbMATHGoogle Scholar
  26. Y. Yang, Z. Su, L. Sun, Medical image enhancement algorithm based on wavelet transform. Electro. Lett. 46(2), 120–121 (2010)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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