29 March 2013 Training system for digital mammographic diagnoses of breast cancer
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Abstract
As the technology evolves, the analog mammography systems are being replaced by digital systems. The digital system uses video monitors as the display of mammographic images instead of the previously used screen-film and negatoscope for analog images. The change in the way of visualizing mammographic images may require a different approach for training the health care professionals in diagnosing the breast cancer with digital mammography. Thus, this paper presents a computational approach to train the health care professionals providing a smooth transition between analog and digital technology also training to use the advantages of digital image processing tools to diagnose the breast cancer. This computational approach consists of a software where is possible to open, process and diagnose a full mammogram case from a database, which has the digital images of each of the mammographic views. The software communicates with a gold standard digital mammogram cases database. This database contains the digital images in Tagged Image File Format (TIFF) and the respective diagnoses according to BI-RADSTM, these files are read by software and shown to the user as needed. There are also some digital image processing tools that can be used to provide better visualization of each single image. The software was built based on a minimalist and a user-friendly interface concept that might help in the smooth transition. It also has an interface for inputting diagnoses from the professional being trained, providing a result feedback. This system has been already completed, but hasn’t been applied to any professional training yet.
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R. L. Thomaz, M. G. Nirschl Crozara, A. C. Patrocinio, "Training system for digital mammographic diagnoses of breast cancer", Proc. SPIE 8674, Medical Imaging 2013: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 86740Q (29 March 2013); doi: 10.1117/12.2008252; https://doi.org/10.1117/12.2008252
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