Performance Analysis of Image Enhancement Techniques for Mammogram Images

  • A. R. Mrunalini
  • J. PremaladhaEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Mammography is a technique which uses X-rays to take mammographic images of the breast, but identifying abnormalities from a mammogram is a challenging task. Many Computer-Aided Diagnosis (CAD) systems are developed to aid the classification of mammograms, as they search in digitized mammographic images for any abnormalities like masses, microcalcification which is difficult to identify especially in dense breasts. The first step in designing a CAD system is preprocessing. It is the process of improving the quality of the image. This paper focuses on the techniques involved in preprocessing the mammogram images to improve its quality for early diagnosis. Preprocessing involves filtering the image, applying image enhancement techniques like Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), Contrast-Limited Adaptive Histogram Equalization (CLAHE), Contrast Stretching, and Bit-plane slicing; filtering techniques like mean, median, Gaussian and Wiener filters are also applied to the mammogram images. The performance of these image enhancement techniques are evaluated using quality metrics, namely Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Contrast-to-Noise Ratio.


Mammogram Preprocessing Enhancement Histogram Filtering 


Compliance to Ethical Standards

Conflict of Interest

Author A. R. Mrunalini, Author J. Premaladha declares that they have no conflict of interest.


We the authors would like to thank the Department of Science and Technology, India for their financial support through Fund for Improvement of S&T Infrastructure (FIST) programme (SR/FST/ETI-349/2013).

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


We the authors sincerely thank the SASTRA Deemed to be University for providing an excellent infrastructure to carry out the research work.


  1. 1.
    Patel BK, Ranjbar S, Wu T, Pockaj BA, Li J, Zhang N, Lobbes M, Zhang B, Mitchell JR (2018) Computer-aided diagnosis of contrast-enhanced spectral mammography: a feasibility study. Eur J Radiol 31(98):207–213CrossRefGoogle Scholar
  2. 2.
    Singh B, Kaur M (2018) An approach for classification of malignant and benign microcalcification clusters. Sādhanā 43(3):39CrossRefGoogle Scholar
  3. 3.
    Khan KB, Khaliq AA, Jalil A, Shahid M (2018) A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising. PLoS ONE 13(2):e0192203CrossRefGoogle Scholar
  4. 4.
    Shastri AA, Tamrakar D, Ahuja K (2018) Density-wise two stage mammogram classification using texture exploiting descriptors. Expert Syst Appl 1(99):71–82CrossRefGoogle Scholar
  5. 5.
    Salem MA, Atef A, Salah A, Shams M (2018) Recent survey on medical image segmentation. In: Computer vision: concepts, methodologies, tools, and applications: concepts, methodologies, tools, and applications 2:129Google Scholar
  6. 6.
    de Moor T, Rodriguez-Ruiz A, Mann R, Teuwen J (2018) Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network. ArXiv preprint arXiv:1802.06865
  7. 7.
    Diniz JO, Diniz PH, Valente TL, Silva AC, de Paiva AC, Gattass M (2018) Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks. Comput Methods Programs BiomedGoogle Scholar
  8. 8.
    George MJ, Sankar SP. Efficient preprocessing filters and mass segmentation techniques for mammogram images. In: 2017 IEEE international conference on circuits and systems (ICCS). IEEE pp 408–413Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of ComputingSASTRA Deemed-to-be-UniversityThanjavurIndia

Personalised recommendations