A Survey of Image Processing Algorithms for Detecting Microcalcification in Mammogram Images
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Abstract
Breast cancer is one of the leading causes of death in women. The breast cancer can be detected through imaging exams as mammography, ultrasonography, magnetic resonance imaging, where mammography is the most common exam. Mammograms (either an analog x-ray film or a digital softcopy) are computationally empowered to extract significant information. It is used to detect and evaluate breast changes. Several computational techniques/algorithms process mammograms to highlight and reveal otherwise unseen features. Thus mammographic images are computationally unfolded to obtain appropriate information that can be used for further analysis. To help radiologists provide an accurate diagnosis, a computer-aided detection (CADe) and computer-aided diagnosis (CADx) algorithms are being developed. This paper gives a survey of image processing algorithms that have been developed for detection different lesions such as calcifications.
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Keywords: Digital Mammography; CAD; microcalcification; wavelet; neural network; support vector machine.
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