The percentage of radiodense (bright) tissue in a mammogram has been correlated to an increased risk of breast cancer. This thesis presents an automated method to quantify the amount of radiodense tissue found in a digitized mammogram. The algorithm employs a radial basis function neural network in order to segment the breast tissue region from the remainder of the X-ray. A spatially varying Neyman-Pearson threshold is used to calculate the percentage of radiodense tissue and compensate for the effects of tissue compression that occurs during a mammography procedure. Results demonstrating the efficacy of the technique are demonstrated by exercising the algorithm on two separate sets of mammograms - one obtained from Brigham Women's Hospital, Harvard Medical School and the other set obtained from Fox Chase Cancer Center and digitized at Rowan University. The results of the algorithm compare favorably with a previously established manual segmentation technique.
","abstract_html":"<p>The percentage of radiodense (bright) tissue in a mammogram has been correlated to an increased risk of breast cancer. This thesis presents an automated method to quantify the amount of radiodense tissue found in a digitized mammogram. The algorithm employs a radial basis function neural network in order to segment the breast tissue region from the remainder of the X-ray. A spatially varying Neyman-Pearson threshold is used to calculate the percentage of radiodense tissue and compensate for the effects of tissue compression that occurs during a mammography procedure. Results demonstrating the efficacy of the technique are demonstrated by exercising the algorithm on two separate sets of mammograms - one obtained from Brigham Women's Hospital, Harvard Medical School and the other set obtained from Fox Chase Cancer Center and digitized at Rowan University. The results of the algorithm compare favorably with a previously established manual segmentation technique.</p>","abstract_has_math":false,"creators":["Eckert, Richard Edson, III"],"institution":null,"degree_name":"M.S. in Engineering","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engineering","degree_department":null,"school":null,"contributors":["Mandayam, Shreekanth"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2003,"date_issued":"2003-12-31T08:00:00Z","date_published":"2003-12-31T08:00:00Z","updated_at":"2026-07-24T04:14:13Z","subjects":["Breast--Examination; Breast--Radiography","Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://rdw.rowan.edu/etd/1292","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mandayam, Shreekanth"]},{"key":"dc:creator","label":"Author","values":["Eckert, Richard Edson, III"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-05-03T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S. in Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Breast--Examination; Breast--Radiography","Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://rdw.rowan.edu/etd/1292"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The percentage of radiodense (bright) tissue in a mammogram has been correlated to an increased risk of breast cancer. This thesis presents an automated method to quantify the amount of radiodense tissue found in a digitized mammogram. The algorithm employs a radial basis function neural network in order to segment the breast tissue region from the remainder of the X-ray. A spatially varying Neyman-Pearson threshold is used to calculate the percentage of radiodense tissue and compensate for the effects of tissue compression that occurs during a mammography procedure. Results demonstrating the efficacy of the technique are demonstrated by exercising the algorithm on two separate sets of mammograms - one obtained from Brigham Women's Hospital, Harvard Medical School and the other set obtained from Fox Chase Cancer Center and digitized at Rowan University. The results of the algorithm compare favorably with a previously established manual segmentation technique.
"]},{"key":"dc:title","label":"Title","values":["Spatially varying threshold models for the automated segmentation of radiodense tissue in digitized mammograms"]}]}],"canonical_facts":{"dc:contributor":["Mandayam, Shreekanth"],"dc:creator":["Eckert, Richard Edson, III"],"dc:date.available":["2016-05-03T07:00:00Z"],"dc:description.abstract":["The percentage of radiodense (bright) tissue in a mammogram has been correlated to an increased risk of breast cancer. This thesis presents an automated method to quantify the amount of radiodense tissue found in a digitized mammogram. The algorithm employs a radial basis function neural network in order to segment the breast tissue region from the remainder of the X-ray. A spatially varying Neyman-Pearson threshold is used to calculate the percentage of radiodense tissue and compensate for the effects of tissue compression that occurs during a mammography procedure. Results demonstrating the efficacy of the technique are demonstrated by exercising the algorithm on two separate sets of mammograms - one obtained from Brigham Women's Hospital, Harvard Medical School and the other set obtained from Fox Chase Cancer Center and digitized at Rowan University. The results of the algorithm compare favorably with a previously established manual segmentation technique.
"],"dc:identifier":["https://rdw.rowan.edu/etd/1292"],"dc:subject":["Breast--Examination; Breast--Radiography","Electrical and Computer Engineering"],"dc:title":["Spatially varying threshold models for the automated segmentation of radiodense tissue in digitized mammograms"],"thesis:degree_discipline":["Electrical & Computer Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S. in Engineering"]},"updated_at":"2026-07-24T04:14:13Z"}