Detection of Suspicious Lesions by Adaptive Thresholding Based on Multiresolution Analysis in Mammograms
Kai Ming Hu, Xieping Gao, Fei Li · IEEE Transactions on Instrumentation and Measurement · 2010
Mammography is the most effective procedure for the early detection of breast cancer. In this paper, we develop a novel algorithm to detect suspicious lesions in mammograms. The algorithm utilizes the combination of adaptive global thresholding segmentation and adaptive local thresholding segmentation on a multiresolution representation of the original mammogram. The algorithm has been verified with 170 mammograms in the Mammographic Image Analysis Society MiniMammographic database. The experimental results show that the detection method has a sensitivity of 91.3% at 0.71 false positives per image.