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Current Medical Imaging


ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Systematic Review Article

Digital Mammograms with Image Enhancement Techniques for Breast Cancer Detection: A Systematic Review

Author(s): Saifullah Harith Suradi* and Kamarul Amin Abdullah

Volume 17, Issue 9, 2021

Published on: 27 January, 2021

Page: [1078 - 1084] Pages: 7

DOI: 10.2174/1573405617666210127101101

Price: $65


Background: Digital mammograms with appropriate image enhancement techniques will improve breast cancer detection, and thus increase the survival rates. The objectives of this study were to systematically review and compare various image enhancement techniques in digital mammograms for breast cancer detection.

Methods: A literature search was conducted with the use of three online databases namely, Web of Science, Scopus, and ScienceDirect. Developed keywords strategy was used to include only the relevant articles. A Population Intervention Comparison Outcomes (PICO) strategy was used to develop the inclusion and exclusion criteria. Image quality was analyzed quantitatively based on peak signal-noise-ratio (PSNR), Mean Squared Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy, and Contrast Improvement Index (CII) values.

Results: Nine studies with four types of image enhancement techniques were included in this study. Two studies used histogram-based, three studies used frequency-based, one study used fuzzy-based and three studies used filter-based techniques. All studies reported PSNR values whilst only four studies reported MSE, AMBE, Entropy, and CII values. Filter-based was the highest PSNR values of 78.93, among other types. For MSE, AMBE, Entropy, and CII values, the highest were frequency-based (7.79), fuzzy-based (93.76), filter-based (7.92), and frequency-based (6.54) respectively.

Conclusion: In summary, image quality for each image enhancement technique is varied, especially for breast cancer detection. In this study, the frequency-based of Fast Discrete Curvelet Transform (FDCT) via the UnequiSpaced Fast Fourier Transform (USFFT) shows the most superior among other image enhancement techniques.

Keywords: Breast cancer screening, breast carcinoma, breast imaging, contrast enhancement, microcalcifications, image processing.

Graphical Abstract
DeSantis CE, Ma J, Gaudet MM, et al. Breast cancer statistics, 2019. CA Cancer J Clin 2019; 69(6): 438-51.
[] [PMID: 31577379]
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68(6): 394-424.
[] [PMID: 30207593]
Zeeshan M, Salam B, Khalid QSB, Alam S, Sayani R. Diagnostic accuracy of digital mammography in the detection of breast cancer. Cureus 2018; 10(4): e2448.
[] [PMID: 29888152]
Wang H, Li JB, Wu L, Gao H. Mammography visual enhancement in CAD-based breast cancer diagnosis. Clin Imaging 2013; 37(2): 273-82.
[] [PMID: 23465979]
Pisano ED, Hendrick RE, Yaffe MJ, et al. DMIST Investigators Group. Diagnostic accuracy of digital versus film mammography: exploratory analysis of selected population subgroups in DMIST. Radiology 2008; 246(2): 376-83.
[] [PMID: 18227537]
Ekpo EU, Alakhras M, Brennan P. Errors in mammography cannot be solved through technology alone. Asian Pac J Cancer Prev 2018; 19(2): 291-301.
[PMID: 29479948]
Abdallah YMY, Elgak S, Zain H, et al. Breast cancer detection using image enhancement and segmentation algorithms. Biomed Res (Aligarh) 2018; 29(20): 3732-6.
Chopra S, Davies EL. Breast cancer. Medicine (United Kingdom) 2020; 48(2): 113-8.
Kumar PM, Kumar RP. Enhancing bio-medical mammography image fusion using optimized genetic algorithm. J Med Imaging Health Inform 2019; 9(3): 502-7.
Singh B, Kaur M. An approach for enhancement of microcalcifications in mammograms. J Med Biol Eng 2017; 37(4): 567-79.
Papadopoulos A, Fotiadis DI, Costaridou L. Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. Comput Biol Med 2008; 38(10): 1045-55.
[] [PMID: 18774128]
Shashi B, Rana SA. Review of medical image enhancement techniques for image processing. Int J Curr Eng Technol 2011; 5(2): 1282-6.
Moher D, Liberati A, Tetzlaff J, Altman DG. PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009; 6(7): e1000097.
[] [PMID: 19621072]
Defining your question: PICO and PS | Resource Details | National Collaborating Centre for Methods and Tools
Clark AF. The mini-MIAS database of mammograms 2012..
Gupta B, Tiwari M. A tool supported approach for brightness preserving contrast enhancement and mass segmentation of mammogram images using histogram modified grey relational analysis. Multidimens Syst Signal Process 2017; 28(4): 1549-67.
Akila K, Jayashree LS, Vasuki A. Mammographic image enhancement using indirect contrast enhancement techniques - A comparative study. Procedia Comput Sci 2015; 47(C): 255-61.
Bhateja V, Misra M, Urooj S. Unsharp masking approaches for HVS based enhancement of mammographic masses: A comparative evaluation. Future Gener Comput Syst 2018; 82: 176-89.
Senthilkumar B, Umamaheswari G. Breast cancer detection using combined curvelet based enhancement and a novel segmentation methods. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2015; 159(1): 83-6.
[] [PMID: 24457833]
Jenifer S, Parasuraman S, Kadirvelu A. Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm. Appl Soft Comput J 2016; 42: 167-77.
Talha M, Sulong GB, Jaffar A. Preprocessing digital breast mammograms using adaptive weighted frost filter. Biomed Res 2016; 27(4): 1407-12.
Reddy GRB, Kumar HP. Enhancement of mammogram images by using entropy improvement approach. SN Appl Sci 2019.
Duan X, Xu Y, Mei Y, et al. A multiscale contrast enhancement for mammogram using dynamic unsharp masking in laplacian pyramid. IEEE Trans Radiat Plasma Med Sci 2018; 3(5): 557-64.
Abdallah YM, Abuhadi NH, Bilal D. Characterisation of breast cancer lesions using image processing based technique. J Clin Diagn Res 2019; 13(8): 9-12.
Pisano ED, Cole EB, Hemminger BM, et al. Image processing algorithms for digital mammography: A pictorial essay. Radiographics 2000; 20: 1479-91.
Hammouche AM. A new FDCT-USFFT and FDCT-Wrap algorithms for image contrast enhancement 2017.
Patel PD, Vijay P, Trivedi K, et al. Image enhancement using fuzzy Technique: Survey and overview. 2014; 154-60.

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