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

Hajar  Moradmand; Saeed Setayeshi; Ali Reza Karimian; Mehri Sirous; Mohammad Esmaeil Akbari

International Journal of Cancer Management

The Official Journal of Cancer Research Center (CRC), Shahid Beheshti University of Medical Sciences

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

Author(s):

Hajar Moradmand^1,*,

Saeed Setayeshi¹,

Ali Reza Karimian²,

Mehri Sirous³,

Mohammad Esmaeil Akbari⁴

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

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

3Isfahan University of Medical Sciences, Isfahan, Iran

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

International Journal of Cancer Management:Vol. 5, issue 2; e80801

Published online:Jun 30, 2012

Article type:Research Article

Received:Nov 12, 2011

Accepted:Feb 07, 2012

How to Cite:Hajar MoradmandSaeed SetayeshiAli Reza Karimian Mehri SirousMohammad Esmaeil AkbariComparing the Performance of Image Enhancement Methods to Detect Microcalcification Clusters in Digital Mammography.Int J Cancer Manag.5(2):e80801.

Abstract

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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