Hajar Moradmand - Department of Biomedical-Radiation Engineering, Amirkabir University of Technology, Tehran, Iran
Saeed Setayeshi - Department of Biomedical-Radiation Engineering, Amirkabir University of Technology, Tehran, Iran
Alireza Karimian - Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
Mehri Sirous - Department of Radiology, Isfahan University of Medical Sciences, Isfahan, Iran

Introduction Breast cancer is one of the most common types of cancer among women.  Early detection of breast cancer is the key to reducing the associated mortality rate. The presence of microcalcifications clusters (MCCs) is one of the earliest signs of breast cancer. Due to poor imaging contrast of mammograms and noise contamination, radiologists may overlook some diagnostic signs, specially the presence of MCCs. In order to improve cancer detection, image enhancement methods are often used to aid radiologists. In this paper, a new enhancement method was presented for the accurate and early detection of MCCs in mammograms. Materials and Methods The proposed system consisted of four main steps including: ۱) image scaling;۲) breast region segmentation;۳) noise cancellation using a filter, which is sensitive to MCCs; and ۴) contrast enhancement of mammograms using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and wavelet transform. To evaluate this method, ۱۲۰ clinical mammograms were used. Results To evaluate the performance of the image enhancement algorithm, contrast improvement index (CII) was used. The proposed enhancement method in this research achieved the highest CII in comparison with other methods applied in this study. The Validity of the results was confirmed by an expert radiologist through visual inspection. Conclusion Detection of MCCs significantly improved in contrast-enhanced mammograms. The proposed method could be helpful for radiologists to easily detect MCCs; it could also decrease the number of biopsies and reduce the frequency of clinical misdiagnosis. Moreover, it could be useful prior to segmentation or classification stages.

Breast Cancer, Image Enhancement, Wavelet Transform