PMID: 16525699Mar 10, 2006Paper

Classification algorithms for microcalcifications in mammograms (Review)

Oncology Reports
E SakkaD Koutsouris

Abstract

Early detection is the key to improve breast cancer prognosis. The only proven effective method of breast cancer early detection is mammography. An early sign of 30-50% of breast cancer is the appearance of clusters of fine, granular microcalcifications and 60-80% of breast carcinomas reveal microcalcification clusters upon histological examination. The high correlation between the appearance of the microcalcification clusters and diseases, proves that computer aided diagnosis (CAD) systems for automated classification of microcalcification clusters will be very useful and helpful for breast cancer control. The fuzzy nature of microcalcification, the low contrast and the low ability of distinguishing them from their surroundings make automated characterization of them extremely difficult. In this study, we give an overview of the currently available literature on characterization of malignant and benign microcalcifications. We compare and evaluate some of the classification algorithms on microcalcifications in mammograms used in various CAD systems, which are separated into categories according to the method in use. Neural networks are used in applications where only a few decisions are required concerning an amount of data. Th...Continue Reading

Citations

Mar 22, 2008·AJR. American Journal of Roentgenology·Keiko ImamuraKentarou Uesugi
Jul 11, 2007·The British Journal of Radiology·A KarahaliouL Costaridou
Jan 22, 2019·Nihon Hoshasen Gijutsu Gakkai zasshi·Chiharu KaiHiroshi Fujita

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