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Feasibility of Determining Breast Density using Processed Mammogram Images

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Title: Feasibility of Determining Breast Density using Processed Mammogram Images
Author: McAvoy, Steven M.
Issue Date: 2010
Publicly Available in cIRcle 2010-07-29
Series/Report no. University of British Columbia, Okanagan campus, Computer Science Undergraduate Honours Essays
Abstract: Breast cancer is one of the most common cancers among women today and one of the most lethal. Breast tissue density has become an increasingly important factor in determining a patient's overall breast cancer risk. With the advent of digital mammography, various computer algorithms have been developed to automate the calculation of breast density. Two types of images are produced from digital mammography machines: the raw image, acquired from the imaging sensor, and the processed image which contains propriety techniques for visual enhancement. Currently, automated breast density algorithms focus on utilizing the raw image while radiologists use the processed image for visual inspection. The processed image is then stored within a patient's medical file. Discovering a means to detect breast density from processed images would allow radiologists to assess retroactively the breast cancer risk of any patient who has previously received a digital mammogram using minimal financial and human resources. An investigation into the feasibility of using an existing breast density algorithm on processed images was explored. The algorithm was modified to accept processed images as input. Thirty-nine craniocaudal mammogram images containing both raw images and their corresponding processed images were used as an experimental control. The breast densities of each image were calculated using both the original and modified algorithms and the resulting correlation was measured. While the modified algorithm did not produce a strong correlation with the original algorithm, evidence suggests further algorithm modifications may lead to a desired outcome.
Affiliation: Psychology and Computer Science (PSCS) (IKBSAS) (Okanagan)
URI: http://hdl.handle.net/2429/27016
Peer Review Status: Unreviewed

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