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      • HARVEST
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      Automated detection of breast cancer using SAXS data and wavelet features

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      Microsoft_Word__Thesis_July_6.pdf (735.8Kb)
      Date
      2005-06-30
      Author
      Erickson, Carissa Michelle
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      The overarching goal of this project was to improve breast cancer screening protocols first by collecting small angle x-ray scattering (SAXS) images from breast biopsy tissue, and second, by applying pattern recognition techniques as a semi-automatic screen. Wavelet based features were generated from the SAXS image data. The features were supplied to a classifier, which sorted the images into distinct groups, such as “normal” and “tumor”. The main problem in the project was to find a set of features that provided sufficient separation for classification into groups of “normal” and “tumor.” In the original SAXS patterns, information useful for classification was obscured. The wavelet maps allowed new scale-based information to be uncovered from each SAXS pattern. The new information was subsequently used to define features that allowed for classification. Several calculations were tested to extract useful features from the wavelet decomposition maps. The wavelet map average intensity feature was selected as the most promising feature. The wavelet map intensity feature was improved by using pre-processing to remove the high central intensities from the SAXS patterns, and by using different wavelet bases for the wavelet decomposition. The investigation undertaken for this project showed very promising results. A classification rate of 100% was achieved for distinguishing between normal samples and tumor samples. The system also showed promising results when tested on unrelated MRI data. In the future, the semi-automatic pattern recognition tool developed for this project could be automated. With a larger set of data for training and testing, the tool could be improved upon and used to assist radiologists in the detection and classification of breast lesions.
      Degree
      Master of Science (M.Sc.)
      Department
      Biomedical Engineering
      Program
      Biomedical Engineering
      Supervisor
      Kendall, Edward J.
      Committee
      Thomlinson, Bill; Gander, Robert; Eramian, Mark G.; Bolton, Ronald J.
      Copyright Date
      June 2005
      URI
      http://hdl.handle.net/10388/etd-07292005-153228
      Subject
      Naive Bayesian Classifier
      Synchrotron
      Diffraction
      Multiresolution
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