Skip to main content

Human Visual System Based Unsharp Masking for Enhancement of Mammograms

  • Chapter
  • First Online:
Non-Linear Filters for Mammogram Enhancement

Part of the book series: Studies in Computational Intelligence ((SCI,volume 861))

  • 220 Accesses

Abstract

It is known that NPF framework consists of a scheme of linear and quadratic filtering counterparts operational as a combo of low- and high-pass filters.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

  • V. Bhateja, M. Misra, S. Urooj, Human visual system based unsharp masking for enhancement of mammographic images. J. Comput. Sci. 21, 387–393 (2017)

    Article  Google Scholar 

  • V. Bhateja, M. Misra, S. Urooj, Unsharp masking approaches for HVS based enhancement of mammograms: a comparative evaluation. Future Gen. Comput. Syst. 82, 176–189 (2018)

    Google Scholar 

  • H.P. Chan, C.J. Vyborny, H.E. MacMahon, C.E. Metz, K. Doi, E.A. Sickles, Digital mammography: ROC studies of the effects of pixels size and unsharp-mask filtering on the detection of subtle micro-calcifications. Investig. Radiol. 22(7), 581–589 (1987)

    Article  Google Scholar 

  • 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. 1–4

    Google Scholar 

  • G. Deng, A generalized unsharp masking algorithm. IEEE Trans. Image Process. 20(5), 1249–1261 (2011)

    Google Scholar 

  • G. Deng, The symmetric generalized LIP model and its application in dynamic range enhancement. J. Math. Imaging Vis. 55(3), 253–265 (2016)

    Article  MathSciNet  Google Scholar 

  • 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)

    Article  Google Scholar 

  • A.J. Evans, A.R.M. Wilson, H.C. Burrell, I.O. Ellis, S.E. Pinder, Mammographic features of ductal carcinoma in situ present on previous mammography. Clin. Radiol. 54(10), 644–646 (1999)

    Google Scholar 

  • C. Florea, C. Vertan, Piecewise linear approximation of logarithmic image processing models for dynamic range enhancement. Buchar. Sci. Bull. Univ. Politeh. Ser. C: Electr. Eng. 71(2), 3–14 (2009)

    Google Scholar 

  • R.C. Gonzalez, R.E. Woods, Digital Image Processing, 3rd edn. (Prentice Hall, USA, 2007)

    Google Scholar 

  • R. Gupta, Siddharth, V. Bhateja, A new unsharp masking algorithm for mammography using non-linear enhancement function, in Proceedings of the International Conference on Information Systems Design and Intelligent Applications (INDIA-2012), Visakhapatnam, India, AISC 132, Springer, January 2012, pp. 779–786

    Google Scholar 

  • M. Jourlin, J.C. Pinoli, Logarithmic image processing: the mathematical and physical framework for the representation and processing of transmitted images. Adv. Imaging Electron Phys. 115, 129–196 (2001)

    Google Scholar 

  • M.K. Kundu, S.K. Pal, Thresholding for edge detection using human psychovisual phenomena. Pattern Recognit. Lett. 4(6), 433–441 (1986)

    Article  Google Scholar 

  • P. KuÅŸ, Ä°. 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)

    Article  Google Scholar 

  • 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)

    Article  MathSciNet  Google Scholar 

  • 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. 2525–2528

    Google Scholar 

  • L. Navarro, G. Deng, G. Courbebaisse, The symmetric logarithmic image processing model. Digit. Signal Process. 23(5), 1337–1343 (2013)

    Article  MathSciNet  Google Scholar 

  • 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)

    Article  Google Scholar 

  • V. Patrascu, V.V. Buzuloiu, The mean dynamic range optimization in the framework of logarithmic models, in Advanced Topics in Optoelectronics, Microelectronics and Nanotechnologies, SPIE 5227, October 2003, pp. 73–80

    Google Scholar 

  • G. Peters, C.M. Jones, K. Daniels, Why is micro-calcification missed on mammography? J. Med. Imaging Radiat. Oncol. 57(1), 32–37 (2013)

    Article  Google Scholar 

  • A. Polesel, G. Ramponi, V.J. Mathews, Image enhancement via adaptive unsharp masking. IEEE Trans. Image Process. 9(3), 505–510 (2000)

    Article  Google Scholar 

  • 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. 1431–1436

    Google Scholar 

  • G. Ramponi, N. Strobel, S.K. Mitra, T. Yu, Nonlinear unsharp masking methods for image contrast enhancement. J. Electron. Imaging 5(3), 353–366 (1996)

    Article  Google Scholar 

  • G. Ramponi, A cubic unsharp masking technique for contrast enhancement. Signal Process. 67(2), 211–222 (1998)

    Article  Google Scholar 

  • G. Ramponi, A. Polesel, Rational unsharp masking technique. J. Electron. Imaging 7(2), 333–338 (1998)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • A.M. Scaranelo, R. Eiada, K. Bukhanov, P. Crystal, Evaluation of breast amorphous calcifications by a computer-aided detection system in full-field digital mammography. Br. J. Radiol. 85(1013), 517–522 (2012)

    Article  Google Scholar 

  • H. Shvayster, S. Peleg, Inversion of picture operators. Pattern Recognit. Lett. 5(1), 49–61 (1987)

    Article  Google Scholar 

  • C. Vertan, A. Oprea, C. Florea, L. Florea, A pseudo-logarithmic image processing framework for edge detection, in: Advanced Concepts for Intelligent Vision Systems, October 2008. LNCS, vol. 5259 (Springer, Berlin, Heidelberg, 2008), pp. 637–644

    Chapter  Google Scholar 

  • E. Wharton, S. Agaian, K. Panetta, A logarithmic measure of image enhancement, in Mobile Multimedia/Image Processing for Military and Security Applications, SPIE 6250, May 2006, pp. 1–15

    Google Scholar 

  • 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. 668–671

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vikrant Bhateja .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bhateja, V., Misra, M., Urooj, S. (2020). Human Visual System Based Unsharp Masking for Enhancement of Mammograms. In: Non-Linear Filters for Mammogram Enhancement. Studies in Computational Intelligence, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-15-0442-6_16

Download citation

Publish with us

Policies and ethics