Performance Analysis of Image Enhancement Techniques for Mammogram Images
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.
KeywordsMammogram 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).
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.
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