Extracting Micro-calcification Clusters on Mammograms for Early Breast Cancer Detection

• Published in: 2006 IEEE International Conference on Information Acquisition pp. 499 - 504
• Main Authors: Yuanjiao Ma, Ziwu Wang, Zheng, J.Z., Lian Lu, Gang Wang, Peng Li, Tianxin Ma, Yinfu Xie
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2006
• Subjects: Breast cancer; Breast neoplasms; Cancer detection; Computer Aided Detection; Data mining; early diagnoses for breast cancer; Hospitals; Malignant tumors; Mammography; micro-calcifications clusters; Relays; X-ray detection; X-ray detectors; X-ray image
• ISBN: 9781424405282; 1424405289
• DOI: 10.1109/ICIA.2006.305784

At present, Mammography is one of the most effective methods to detect early breast cancers. However, the signs of most micro-calcifications that are the early signs of malignant tumours cannot appear clearly in an inhomogeneous background because of the complicated structures in breast. As a rule, a tool of magnified glass is required to enlarge mammograph views to check the characteristics of a suspected lesion, but there is at least 15 percent of breast cancer that is still missed. Without specific feature, when doctors have to relay on clinical histology, this will causes a large number of misdiagnosis with false negative or false positive even decrease the rate of detection, furthermore, causing pain to the patients. Therefore, it is a crucial task to need a reliable and effective approach to assist the micro-calcifications detection. This paper uses a new technology to extract micro-calcifications clusters with accurate edge effects to obtain much more hidden information which can't be detected by the naked eye on mammograms in order to help the doctors in diagnosing early breast cancer. In this paper, this research combines the findings of histopathology in benign or malignant calcifications and uses the typical application examples with the contrastive analysis to do a relevant expatiation.