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Semi-automated search for abnormalities in mammographic X-ray images

dc.contributor.advisorKendall, Edward J.en_US
dc.contributor.committeeMemberEramian, Mark G.en_US
dc.contributor.committeeMemberDick, Raineren_US
dc.contributor.committeeMemberDegenstein, Douglas A.en_US
dc.contributor.committeeMemberBolton, Ronald J.en_US
dc.contributor.committeeMemberManson, Alanen_US
dc.contributor.committeeMemberPywell, Robert E.en_US
dc.creatorBarnett, Michael Gordonen_US 2006en_US
dc.description.abstractBreast cancer is the most commonly diagnosed cancer among Canadian women; x-ray mammography is the leading screening technique for early detection. This work introduces a semi-automated technique for analyzing mammographic x-ray images to measure their degree of suspiciousness for containing abnormalities. The designed system applies the discrete wavelet transform to parse the images and extracts statistical features that characterize an image’s content, such as the mean intensity and the skewness of the intensity. A naïve Bayesian classifier uses these features to classify the images, achieving sensitivities as high as 99.5% for a data set containing 1714 images. To generate confidence levels, multiple classifiers are combined in three possible ways: a sequential series of classifiers, a vote-taking scheme of classifiers, and a network of classifiers tuned to detect particular types of abnormalities. The third method offers sensitivities of 99.85% or higher with specificities above 60%, making it an ideal candidate for pre-screening images. Two confidence level measures are developed: first, a real confidence level measures the true probability that an image was suspicious; and second, a normalized confidence level assumes that normal and suspicious images were equally likely to occur. The second confidence measure allows for more flexibility and could be combined with other factors, such as patient age and family history, to give a better true confidence level than assuming a uniform incidence rate. The system achieves sensitivities exceeding those in other current approaches while maintaining reasonable specificity, especially for the sequential series of classifiers and for the network of tuned classifiers.en_US
dc.subjectmass detectionen_US
dc.subjectpattern recognitionen_US
dc.subjectcomputer aided detectionen_US
dc.subjectbreast canceren_US
dc.subjectbayes classifieren_US
dc.subjectx-ray imagingen_US
dc.subjectcalcification detectionen_US
dc.titleSemi-automated search for abnormalities in mammographic X-ray imagesen_US
dc.type.materialtexten_US and Engineering Physicsen_US and Engineering Physicsen_US of Saskatchewanen_US of Science (M.Sc.)en_US


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