Screening mammography has proven to be an effective procedure in identifying early breast cancer. The cancers found by mammography tend to be smaller and of less advanced stages than those found by breast physical examination. Smaller and lower stage breast cancers have better survival rates. Unfortunately, approximately 10% of breast cancers are not visible with mammography, particularly in patients with large amounts of breast glandular tissue. If we can increase the sensitivity of mammography by making perceivable the recorded information through greyscale manipulation techniques (i.e. """"""""contrast-enhancement"""""""" or """"""""image processing"""""""") so that these cancers are more readily apparent, early detection may result in significantly reduced mortality in this population.
The aim of this proposal is to evaluate different methods of transferring the data recorded in a mammogram to displayed intensities, in order to increase the sensitivity of mammography and facilitate earlier identification of breast cancer. The proposed research has three parts: first, we will provide initial greyscale processing methods to augment a standard display of the digital images at the clinical sites. These initial methods will come from results of experiments conducted at UNC on preset intensity windows, and development of softcopy display with appropriate ergonomics. Second, we will apply our image display processing methods to a series of prospective clinical studies to evaluate the affect of various static and dynamic image processing methods on the detection of abnormalities in the digitally acquired mammograms from the two clinical sites (MGDM and TJUDM). Improved image processing algorithms and softcopy ergonomics will be provided to the clinical sites throughout the grant period as they are available. Third, we will conduct a controlled observer study to determine whether a digital mammogram, compressed and decompressed using a lossy compression algorithms provided by the GE telemammography group, contains the same clinical information as the original image.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Diagnostic Radiology Study Section (RNM)
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University of North Carolina Chapel Hill
Schools of Medicine
Chapel Hill
United States
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Hemminger, Bradley M; Bauers, Anne; Yang, Jian (2008) Comparison of navigation techniques for large digital images. J Digit Imaging 21 Suppl 1:S13-38
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