Skip to main content

A Novel Improved Method of RMSHE-Based Technique for Mammography Images Enhancement

  • Conference paper
  • First Online:
Fundamental Research in Electrical Engineering

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 480))

Abstract

Contrast improvement is one of the most important steps in medical image enhancement procedures such as mammography. In this paper, a combination of best features related to direct and indirect histogram equalization techniques is proposed in a two dimensional workspace. Using different advantages of these methods, while the proposed algorithm is able to improve the contrast and brightness of mammography images, it could decrease different effects of noises, too. On the other hand, in order to reduce undesirable effects of traditional histogram equalization techniques, an improvement of recursive mean-separate histogram equalization using a fusion of contrast-limited adaptive histogram equalization is proposed, too. Evaluation results using four effective measurement techniques e.g. peak signal-to-noise ratio, mean squared error, absolute mean brightness error and effective measure of enhancement, shows that the suggested method has significant results in contrast enhancement.

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 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
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

  1. Tang J, Rangayyan RM, Xu J, El Naqa I, Yang Y (2009) Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans Inf Technol Biomed 13(2): 236–251

    Google Scholar 

  2. Akila K, Jayashree LS, Vasuki A (2015) Mammographic image enhancement using indirect contrast enhancement techniques—a comparative study. Procedia Comput Sci 47:255–261

    Article  Google Scholar 

  3. Athanasiou A, Aubert E, Vincent Salomon A, Tardivon A (2014) Complex cystic breast masses in ultrasound examination. Diagn Intervent Imaging 95:169–179

    Article  Google Scholar 

  4. Shao Y-Z, Liu L-Z, Bie M-J, Li C-c, Wu Y-p, Xie X-m, Li L (2011) Characterizing the clustered microcalcifications on mammograms to predict the pathological classification and grading: a mathematical modeling approach, Published online: 22 April 2011 Society for Imaging Informatics in Medicine

    Article  Google Scholar 

  5. Popli MB, Teotia R, Narang M, Krishna H (2014) Breast positioning during mammography: mistakes to be avoided. Breast Cancer: Basic Clin Res 8

    Google Scholar 

  6. Zhou Y, Panetta K, Agaian S (2010) Human visual system based mammogram enhancement and analysis. In: 2010 2nd international conference on image processing theory tools and applications (IPTA). IEEE, pp 229–234

    Google Scholar 

  7. Suhail Z, Denton ERE, Zwiggelaar R (2017) Tree-based modelling for the classification of mammographic benign and malignant micro-calcification clusters. Multimed Tools Appl. Published with open access at Springer

    Article  Google Scholar 

  8. Bilous M (2010) Breast core needle biopsy: issues and controversies. Mod Pathol 23:S36–S45

    Article  Google Scholar 

  9. Sivaramakrishna R, Obuchowski NA, Chilcote WA, Cardenosa G, Powell KA (2000) Comparing the performance of mammographic enhancement algorithms: a preference study. Am J Roentgenol 175(1):45–51

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Cheng H-D, Xu H (2000) A novel fuzzy logic approach to contrast enhancement. Pattern Recogn 33(5):809–819

    Article  Google Scholar 

  12. Agaian S, Panetta K, Grigoryan A (2001) Transform based image enhancement algorithms with performance measure. IEEE Trans Image Process 10(3):367–382

    Article  Google Scholar 

  13. Tang J, Peli E, Acton S (2003) Image enhancement using a contrast measure in the compressed domain. IEEE Sig Process Lett 10(10):289–292

    Article  Google Scholar 

  14. Kim YT (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron 43(1):1–8

    Article  Google Scholar 

  15. Wang Q, Ward RK (2007) Fast image/video contrast enhancement based on weighted thresholded histogram equalization. IEEE Trans Consum Electron 53(2):757–764

    Article  Google Scholar 

  16. Hashemi S, Kiani S, Noroozi N, Moghaddam ME (2010) Contrast enhancement method based on genetic algorithm. Pattern Recogn Lett 31:1816–1824

    Article  Google Scholar 

  17. Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice- Hall, Englewood Cliffs

    Google Scholar 

  18. Pisano ED, Cole EB, Hemminger BM, Yaffe M, Aylward SR, Maidment ADA, Eugene Johnston R et al (2000) Image processing algorithms for digital mammography: a pictorial essay 1. Radiographics 20(5):1479–1491

    Article  Google Scholar 

  19. Al-Ameen Z, Sulong G, Rehman A, Al-Dhelaan A, Saba T (2015) An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization. EURASIP J Adv Sig Process 2015:32. https://doi.org/10.1186/s13634-015-0214-1

  20. Pisano ED, Zong S, Hemminger BM, DeLuca M, Johnston RE, Muller K, Patricia Braeuning M, Pizer SM (1998) Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J Digit Imaging 11(4): 193–200

    Article  Google Scholar 

  21. Gupta P, Kumare JS, Singh UP, Singh RK (2017) Histogram based image enhancement techniques: a survey. Int J Comput Sci Eng 5(6). E-ISSN: 2347-2693

    Google Scholar 

  22. Jayaraman S, Esakkirajan S, Veerakumar T (2015) Digital image processing. Tata McGraw- Hill Education Pvt. Ltd

    Google Scholar 

  23. Kim JY, Kim L, Hwang S (2001) An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Trans Circ Syst Video Technol: 475–484

    Google Scholar 

  24. Haddadnia J, Seryasat OR, Ghayoumi-Zadeh H, Rabiee H (2015) An efficient method for detection of masses in mammogram images. Cumhuriyet Sci J 36(3):2269–2277

    Google Scholar 

  25. Seryasat OR, Haddadnia J, Ghayoumi Zadeh H (2016) Assessment of a novel computer aided mass diagnosis system in mammograms. Iran J Breast Dis 9(3):31–41

    Google Scholar 

  26. Chen S-D, Ramli AR (2003) Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans Consum Electron 49(4):1301–1309

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Younes Mousania .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mousania, Y., Karimi, S. (2019). A Novel Improved Method of RMSHE-Based Technique for Mammography Images Enhancement. In: Montaser Kouhsari, S. (eds) Fundamental Research in Electrical Engineering. Lecture Notes in Electrical Engineering, vol 480. Springer, Singapore. https://doi.org/10.1007/978-981-10-8672-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-8672-4_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8671-7

  • Online ISBN: 978-981-10-8672-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics