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Non-Linear Polynomial Filters for Edge Enhancement of Mammograms

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

Abstract

Non-linear polynomial filtering (NPF) framework has been explored previously as a robust approach for contrast improvement of mammographic images. However, NPF ‘Prototypes: α and β′ have been performance limited; as the contrast improvement has been accompanied with a severe background suppression in mammograms. This affected the visualization of other anatomical structures and diagnostic features in the vicinity of the ROI; these features equally contribute towards diagnostic decision making by radiologists. On the other hand, it is equally difficult to improve the edge strength and sharpness of the ROI without compromising the background content.

References

  1. Anand, S., Kumari, R.S., Jeeva, S., Thivya, T.: Directionlet transform based sharpening and enhancement of mammographic X-ray images. Biomed. Signal Process. Control 8(4), 391–399 (2013)CrossRefGoogle Scholar
  2. M. Basu, Gaussian based edge-detection methods—a survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 32, 252–260 (2002)CrossRefGoogle Scholar
  3. V. Bhateja, S. Devi, An improved non-linear transformation function for enhancement of mammographic breast masses, in Proceedings of (IEEE) 3rd International Conference on Electronics & Computer Technology (ICECT-2011), Kanyakumari (India), vol. 5 (April 2011a), pp. 341–346Google Scholar
  4. V. Bhateja, S. Devi, A novel framework for edge detection of microcalcifications using a non-linear enhancement operator and morphological filter, in Proceedings of (IEEE) 3rd International Conference on Electronics & Computer Technology (ICECT-2011), Kanyakumari (India), vol. 5 (April 2011b), pp. 419–424Google Scholar
  5. V. Bhateja, S. Devi, S. Urooj, An Evaluation of edge detection algorithms for mammographic calcifications, in Proceedings of (Springer) 4th International Conference on Signal and Image Processing (ICSIP 2012), Coimbatore, India, vol. 2 (Dec 2012), pp. 487–498Google Scholar
  6. V. Bhateja, M. Misra, S. Urooj, Non-Linear polynomial filters for edge enhancement of mammogram lesions. Comput. Methods Programs Bio-med. 129C, 125–134 (2016)CrossRefGoogle Scholar
  7. 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 (Oct 2011), pp. 3167–3172Google Scholar
  8. G. Deng, A generalized unsharp masking algorithm. IEEE Trans. Image Process. 20(5), 1249–1261 (2011)Google Scholar
  9. M.L. Giger, Computer-aided diagnosis of breast lesions in medical images. Comput. Sci. Eng. 2(5), 39–45 (2000)CrossRefGoogle Scholar
  10. R.C. Gonzalez, R.E. Woods, Digital Image Processing, 3rd edn. (Prentice Hall, New York, 2007)Google Scholar
  11. P. Görgel, A. Sertbas, O.N. Uçan, Computer-aided classification of breast masses in mammogram images based on spherical wavelet transform and support vector machines. Expert Syst. 32(1), 155–164 (2015)CrossRefGoogle Scholar
  12. 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, pp. 85–89 (2012)Google Scholar
  13. H.S Jagannath, J. Virmani, V. Kumar, Morphological enhancement of microcalcifications in digital mammograms. J. Inst. Eng. (India) Ser. B 93(3), 163–172 (2012)CrossRefGoogle Scholar
  14. M. Jourlin, J.-C. Pinoli, A model for logarithmic image processing. J. Microscopy 149(1), 21–35 (1988)CrossRefGoogle Scholar
  15. M. Jourlin, J.-C. Pinoli, Image dynamic range enhancement and stabilization in the context of the logarithmic image processing model. Signal Process. 41(2), 225–237 (1995)zbMATHCrossRefGoogle Scholar
  16. M. Jourlin, J.C. Pinoli, Logarithmic image processing: the mathematical and physical framework for the representation and processing of transmitted images. Adv. Imag. Electron Phys. 115, 129–196 (2001)Google Scholar
  17. M. Jourlin, J.-C. Pinoli, R. Zeboudj, Contrast definition and contour detection for logarithmic images. J. Microscopy 156(1), 33–40 (1989)CrossRefGoogle Scholar
  18. W. Lu, R. Dou, G. Zhang, A new method for extracting region of interest in mammograms, in Proceedings of IEEE International Conference in Medical Imaging Physics and Engineering (ICMIPE-2013), Shenyang, China (Oct 2013), pp. 228–230Google Scholar
  19. E. Matsuyama, D.Y. Tsai, Y. Lee, M. Tsurumaki, N. Takahashi, H. Watanabe, H.M. Chen, A modified undecimated discrete wavelet transform based approach to mammographic image denoising. J. Digital Imag. 26(4), 748–758 (2013)CrossRefGoogle Scholar
  20. S.K. Mitra, G.L. Sicuranza, Nonlinear Image Processing (Academic Press, New York, 2001)Google Scholar
  21. A. Monin, G. Salut, IIR volterra filtering with application to bilinear systems. IEEE Trans. Signal Process. 44(9), 2209–2221 (1996)CrossRefGoogle Scholar
  22. L. Navarro, G. Deng, G. Courbebaisse, The symmetric logarithmic image processing model. Digital Signal Process. 23(5), 1337–1343 (2013)MathSciNetCrossRefGoogle Scholar
  23. K.A. Panetta, E.J. Wharton, S.S. Agaian, Human visual system-based image enhancement and logarithmic contrast measure. IEEE Trans. Syst. Man Cybern. B Cybern. 38(1), 174–188 (2008)CrossRefGoogle Scholar
  24. K.A. Panetta, Z. Yicong, S.S. Agaian, H. Jia, Non-linear unsharp masking for mammogram enhancement. IEEE Trans. Inform. Technol. Biomed. 15(6), 918–928 (2011)CrossRefGoogle Scholar
  25. K.-S. Peng, Efficient image resolution enhancement using edge-directed unsharp masking sharpening for real-time ASIC applications. J. Comput. Sci. Syst. Biol. 8(3), 174–184 (2015)Google Scholar
  26. W.K. Pratt, Image enhancement, in Digital Image Processing: PIKS Scientific Inside, 4th edn. (2001), pp. 247–305Google Scholar
  27. G. Ramponi, Bi-impulse response design of isotropic quadratic filters. Proc. IEEE 78(4), 665–667 (1990)CrossRefGoogle Scholar
  28. K. Rezaee, J. Haddadnia, Designing an algorithm for cancerous tissue segmentation using adaptive k-means cluttering and discrete wavelet transform. J. Biomed. Phys. Eng. 3(3), 93–104 (2013)Google Scholar
  29. L. Septiana, G.H. Lin, W.C. Lin, K.P. Lin, Mammogram enhancement using anisotropic diffusion and weigthed k-means clustering. Trans. Japan. Soc. Med. Biol. Eng. 51(Supplement), R-89 (2013)Google Scholar
  30. Siddhartha, R. Gupta, V. Bhateja, An improved unsharp masking algorithm for enhancement of mammographic masses, in Proceedings of IEEE Students Conference on Engineering and Systems (SCES-2012), Allahabad, India (Mar 2012), pp. 234–237Google Scholar
  31. S. Singh, K. Bovis, An evaluation of contrast enhancement techniques for mammographic breast masses. IEEE Trans. Inform. Technol. Biomed. 9(1), 109–119 (2005)CrossRefGoogle Scholar
  32. S. Srivastava, N. Sharma, S. Singh, R. Srivastava, A combined approach for the enhancement and segmentation of mammograms using modified fuzzy c-means method in wavelet domain. J. Med. Phys. Assoc. Med. Physicists India 39(3), 169–183 (2014)CrossRefGoogle Scholar
  33. J.A. Stark, Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans. Image Process. 9(5), 889–896 (2000)CrossRefGoogle Scholar
  34. 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
  35. M. Trivedi, A. Jaiswal, V. Bhateja, A new contrast measurement index based on logarithmic image processing model, in Proceedings of International Conference on Frontiers in Intelligent Computing Theory and Applications (FICTA-2012), Bhubaneswar, India (Springer, Berlin, AISC 199, 2012), pp. 715–723Google Scholar
  36. V. Vizireanu, R. Udrea, Visual-oriented morphological foreground content grayscale frames interpolation method. J. Electron. Imag. 18(2), 020502-1–020502-3 (2009)CrossRefGoogle Scholar
  37. Y.X. Xinfeng, D. Shumin, Research on image enhancement algorithm with restrain noise function. Open Cybern. Syst. J. 9(1), 546–555 (2015)CrossRefGoogle Scholar
  38. M.J. Yaffe, Detectors for digital mammography, in Digital Mammography, ed. by U. Bick, F. Diekmann (Springer, Berlin, 2010), pp. 13–31CrossRefGoogle Scholar
  39. M.J. Yaffe, J.G. Mainprize, Detectors for digital mammography. Technol. Cancer Res. Treat. 3(4), 309–324 (2004)CrossRefGoogle Scholar
  40. 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 (Sept 2009), pp. 3681–3684Google Scholar

Copyright information

© 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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