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Comparing the Performance of Image Enhancement Methods to Detect Microcalcification Clusters in Digital Mammography


avatar Hajar Moradmand 1 , * , avatar Saeed Setayeshi 1 , avatar Ali Reza Karimian 2 , avatar Mehri Sirous 3 , avatar Mohammad Esmaeil Akbari 4

1 Dept. of Biomedical Radiation Engineering, Amirkabir University of Technology, Tehran, Iran

2 Dept. of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran

3 Isfahan University of Medical Sciences, Isfahan, Iran

4 Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

How to Cite: Moradmand H , Setayeshi S, Karimian A R, Sirous M, Akbari M E. Comparing the Performance of Image Enhancement Methods to Detect Microcalcification Clusters in Digital Mammography. Int J Cancer Manag. 2012;5(2):e80801.


International Journal of Cancer Management: 5 (2); e80801
Published Online: June 30, 2012
Article Type: Research Article
Received: November 12, 2011
Accepted: February 07, 2012


Background: Mammography is the primary imaging technique for detection and diagnosis of breast cancer; however, the contrast of a mammogram image is often poor, especially for dense and glandular tissues. In these cases the radiologist may miss some diagnostically important microcalcifications. In order to improve diagnosis of cancer correctly, image enhancement technology is often used to enhance the image and help radiologists.
Methods: This paper presents a comparative study in digital mammography image enhancement based on four different algorithms: wavelet-based enhancement (Asymmetric Daubechies of order 8), Contrast-Limited Adaptive Histogram Equalization (CLAHE), morphological operators and unsharp masking. These algorithms have been tested on 114 clinical digital mammography images. The comparison for all the proposed image enhancement techniques was carried out to find out the best technique in enhancement of the mammogram images to detect microcalcifications.
Results: For evaluation of performance of image enhancement algorithms, the Contrast Improvement Index (CII) and profile intensity surface area distribution curve quality assessment have been used after any enhancement. The results of this study have shown that the average of CII is about 2.61 for wavelet and for CLAHE, unsharp masking and morphology operation are about 2.047, 1.63 and 1.315 respectively.
Conclusion: Experimental results strongly suggest that the wavelet transformation can be more effective and improve significantly overall detection of the Computer-Aided Diagnosis (CAD) system especially for dense breast. Compare to other studies, our method achieved a higher CII.


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© 2012, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.