6 March 2002 Image processing, radiological, and clinical information fusion in breast cancer detection
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Screening mammography is the most efficient and cost-effective method available for detecting the signs of early breast cancer in asymptomatic women between the ages of 50 and 69. To improve the detection rate and reduce the number of unnecessary biopsies, many different computer-aided diagnosis techniques have been developed. Many of these techniques use image processing algorithms to automatically segment and classify the images. The decision-making process associated with the evaluation of mammograms is complex and incorporates multiple sources of information from standard medical knowledge and radiology to pathology. The use of this information combined with the results of image processing offers new challenges to the field of data and information fusion. In this paper, we describe the different information sources and their data as well as the framework that is needed to support this type of fusion. A database of breast cancer screening cases forms the basis of the resulting fusion model. The database and decision-level fusion techniques will facilitate unique and specialized approaches for efficient and sophisticated diagnosis of breast cancer.
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Hilary Alto, Hilary Alto, Rangaraj M. Rangayyan, Rangaraj M. Rangayyan, Basel Solaiman, Basel Solaiman, J. E. Leo Desautels, J. E. Leo Desautels, J. H. MacGregor, J. H. MacGregor, } "Image processing, radiological, and clinical information fusion in breast cancer detection", Proc. SPIE 4731, Sensor Fusion: Architectures, Algorithms, and Applications VI, (6 March 2002); doi: 10.1117/12.458377; https://doi.org/10.1117/12.458377

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