RICE: A method for quantitative mammographic image enhancement |
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| Affiliation: | 1. School of Computer Science, University of Lincoln, Issac Newton Building, Bradyford Pool LN6 7TS, United Kingdom;2. Department of Oncological Imaging, University of Oxford, Old Road Campus Research Building, Headington, Oxford OX3 7DQ, United Kingdom;1. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA;2. Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA;3. Department of Orthopedic Surgery, Johns Hopkins Hospital, Baltimore, USA;4. Laboratory for Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany;1. Medical School of Nanjing University, Nanjing, China;2. National Institute of Healthcare Data Science at Nanjing University, Nanjing, China;3. School of Mathematics and Statistics, Xi’an Jiaotong University, Shanxi, China;4. Department of Psychiatry and Behavioral Sciences and the Department of Computer Science, Stanford University, CA, USA;5. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;6. Department of Radiology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China;7. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China;8. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China;9. Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea;1. Department of medical Informatics, Arizona State University, Scottsdale, AZ 85259, USA;2. Division of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, AZ 85259, USA;3. Department of Radiology, Mayo Clinic, Scottsdale, AZ 85259, USA;1. Department of Artificial Intelligence, Xiamen University, Xiamen, Fujian, China;2. Department of Ultrasound, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen Third Peoples Hospital, Shenzhen, Guangdong, China;3. Digital Image Group (DIG), London, ON, Canada;4. School of Biomedical Engineering, Western University, London, ON, Canada |
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| Abstract: | We introduce Region of Interest Contrast Enhancement (RICE) to identify focal densities in mammograms. It aims to help radiologists: 1) enhancing the contrast of mammographic images; and 2) detecting regions of interest (such as focal densities) that are candidate masses potentially masked behind dense parenchyma. Cancer masking is an unsolved issue, particularly in breast density categories BI-RADS C and D. RICE suppresses normal breast parenchyma in order to highlight focal densities. Unlike methods that enhance mammograms by modifying the dynamic range of an image; RICE relies on the actual tissue composition of the breast. It segments Volumetric Breast Density (VBD) maps into smaller regions and then applies a recursive mechanism to estimate the ‘neighbourhood’ for each segment. The method then subtracts and updates the neighbourhood, or the encompassing tissue, from each piecewise constant component of the breast image. This not only enhances the appearance of a candidate mass but also helps in estimating the mass density. In extensive experiments, RICE enhances focal densities in all breast density types including the most challenging category BI-RADS D. Suitably adapted, RICE can be used as a precursor to any computer-aided diagnostics and detection system. |
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