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

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      Michael_Gordon_Barnett_M_Sc_Thesis.pdf (3.170Mb)
      Date
      2006-09-21
      Author
      Barnett, Michael Gordon
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      Breast 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.
      Degree
      Master of Science (M.Sc.)
      Department
      Physics and Engineering Physics
      Program
      Physics and Engineering Physics
      Supervisor
      Kendall, Edward J.
      Committee
      Eramian, Mark G.; Dick, Rainer; Degenstein, Douglas A.; Bolton, Ronald J.; Manson, Alan; Pywell, Robert E.
      Copyright Date
      September 2006
      URI
      http://hdl.handle.net/10388/etd-10192006-201550
      Subject
      mass detection
      pattern recognition
      biophysics
      computer aided detection
      wavelet
      breast cancer
      bayes classifier
      mammogram
      x-ray imaging
      calcification detection
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