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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
Akila K, Jayashree LS, Vasuki A (2015) Mammographic image enhancement using indirect contrast enhancement techniques—a comparative study. Procedia Comput Sci 47:255–261
Athanasiou A, Aubert E, Vincent Salomon A, Tardivon A (2014) Complex cystic breast masses in ultrasound examination. Diagn Intervent Imaging 95:169–179
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
Popli MB, Teotia R, Narang M, Krishna H (2014) Breast positioning during mammography: mistakes to be avoided. Breast Cancer: Basic Clin Res 8
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
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
Bilous M (2010) Breast core needle biopsy: issues and controversies. Mod Pathol 23:S36–S45
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
Polesel A, Ramponi G, Mathews V (2000) Image enhancement via adaptive unsharp masking. IEEE Trans Image Process 9(3):505–510
Cheng H-D, Xu H (2000) A novel fuzzy logic approach to contrast enhancement. Pattern Recogn 33(5):809–819
Agaian S, Panetta K, Grigoryan A (2001) Transform based image enhancement algorithms with performance measure. IEEE Trans Image Process 10(3):367–382
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
Kim YT (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron 43(1):1–8
Wang Q, Ward RK (2007) Fast image/video contrast enhancement based on weighted thresholded histogram equalization. IEEE Trans Consum Electron 53(2):757–764
Hashemi S, Kiani S, Noroozi N, Moghaddam ME (2010) Contrast enhancement method based on genetic algorithm. Pattern Recogn Lett 31:1816–1824
Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice- Hall, Englewood Cliffs
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
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
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
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
Jayaraman S, Esakkirajan S, Veerakumar T (2015) Digital image processing. Tata McGraw- Hill Education Pvt. Ltd
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
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
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
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
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)