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Breast tumor detection in digital mammography is one of the most important methods of breast cancer prevention. Computer-aided diagnosis (CAD) based on extreme learning machine (ELM) has significant meanings for breast tumor detection as it has good generalization abilities and a high learning efficiency. In this paper, a breast tumor detection algorithm in digital mammography based on ELM is proposed...
The segmentation of breast mass is an important step not only in quantitative diagnosis but also in Computer-Aided Diagnosis (CAD) for the mammography. The objective of this study is to develop a reliable, reproducible and automatic segmentation method, which can provide the mass contours for quantitative analysis and also can be further utilized for feature extraction in CAD system. This automatic...
As is well known, microcalcification cluster is one of the early and important signs of breast cancer. However, microcalcifications with low contrast can hardly be distinguished by physicians sometimes, and CAD system plays a more and more important role in clinical diagnosis. An integrated detection method for microcalcification clusters in digital mammography was proposed in this paper. It consisted...
High accurate detection of mass in mammogram is critical for improving the performance and efficiency of computer-aided diagnosis (CAD) system. In this paper, we propose a novel approach to enhance the detection performance of mass in mammograms using Wavelet Transform Modulus Maximum (WTMM). First, hunt the region of interest (ROI) through the whole image and the ROI was approximately located by...
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