Type of Document	Dissertation
Author	Sample, John Terry
Author's Email Address	jsampl1@lsu.edu
URN	etd-0708103-073122
Title	Computer Assisted Screening of Digital Mammogram Images
Degree	Doctor of Philosophy (Ph.D.)
Department	Computer Science
Advisory Committee	Advisor Name Title John M. Tyler Committee Chair Donald Kraft Committee Member S. S. Iyengar Committee Member Young Chun Committee Member Erno Sajo Dean's Representative
Keywords	segmentation mass location
Date of Defense	2003-07-07
Availability	unrestricted
Abstract 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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