| Summary: | The use of computer systems to assist clinicians in digital mammography image screening has advantages over traditional methods. Computer algorithms can enhance the appearance of the images and highlight suspicious areas. Screening provides a more thorough examination of the images. Any computer system that does screening of digital mammograms contains components to address multiple tasks such as: image segmentation, mass lesion detection and classification, and microcalcification detection and classification.
This dissertation provides both effective and efficient improvements to existing algorithms, which segment mammogram images and locate mass lesions. In addition, we provide a new algorithm to evaluate and report the results for mass lesion detection.
The algorithm presented for mammogram segmentation uses a histogram based operator to define the boundaries between the different components of a mammogram image. It employs a unique clustering algorithm to produce closed, labeled sets of pixels which represent the distinct image components.
The mass location algorithm uses a variation of template matching to locate suspicious areas. An evaluation of potential templates and algorithms is included. The method for testing and recording the results of the mass location algorithm groups suspicious pixels into regions and then compares them to the pathology.
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