{"id":{"repo_id":"unlv","oai_identifier":"oai:oasis.library.unlv.edu:rtds-1839"},"canonical_url":"https://search.dev.ndltd.org/etd/unlv/oai:oasis.library.unlv.edu:rtds-1839","repository":{"repo_id":"unlv","name":"University of Nevada - Las Vegas","base_url":"https://oasis.library.unlv.edu/do/oai/"},"display":{"title":"Effects of discrete wavelet compression on automated mammographic shape recognition","abstract":"At present early detection is critical for the cure of breast cancer. Mammography is a breast screening technique which can detect breast cancer at the earliest possible stage. Mammographic lesions are typically classified into three shape classes, namely round, nodular and stellate. Presently this classification is done by experienced radiologists. In order to increase the speed and decrease the cost of diagnosis, automated recognition systems are being developed. This study analyses an automated classification procedure and its sensitivity to wavelet based image compression; In this study, the mammographic shape images are compressed using discrete wavelet compression and then classified using statistical classification methods. First, one dimensional compression is done on the radial distance measure and the shape features are extracted. Second, linear discriminant analysis is used to compute the weightings of the features. Third, a minimum distance Euclidean classifier and the leave-one-out test method is used for classification. Lastly, a two dimensional compression is performed on the images, and the above process of feature extraction and classification is repeated. The results are compared with those obtained with uncompressed mammographic images.","abstract_html":"At present early detection is critical for the cure of breast cancer. Mammography is a breast screening technique which can detect breast cancer at the earliest possible stage. Mammographic lesions are typically classified into three shape classes, namely round, nodular and stellate. Presently this classification is done by experienced radiologists. In order to increase the speed and decrease the cost of diagnosis, automated recognition systems are being developed. This study analyses an automated classification procedure and its sensitivity to wavelet based image compression; In this study, the mammographic shape images are compressed using discrete wavelet compression and then classified using statistical classification methods. First, one dimensional compression is done on the radial distance measure and the shape features are extracted. Second, linear discriminant analysis is used to compute the weightings of the features. Third, a minimum distance Euclidean classifier and the leave-one-out test method is used for classification. Lastly, a two dimensional compression is performed on the images, and the above process of feature extraction and classification is repeated. The results are compared with those obtained with uncompressed mammographic images.","abstract_has_math":false,"creators":["Kalluri, Ravikiran"],"institution":"University of Nevada, Las Vegas","degree_name":"Master of Electrical Engineering (MEE)","degree_level":"Thesis","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Lori Bruce"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1998,"date_issued":"1998-01-01T08:00:00Z","date_published":"1998-01-01T08:00:00Z","updated_at":"2026-07-24T05:24:45Z","subjects":[],"languages":[],"rights":["IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://oasis.library.unlv.edu/rtds/840"],"render_values":[{"text":"https://oasis.library.unlv.edu/rtds/840","href":"https://oasis.library.unlv.edu/rtds/840","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.25669/q5jv-m9f1","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lori Bruce"]},{"key":"dc:creator","label":"Author","values":["Kalluri, Ravikiran"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["University of Nevada, Las Vegas"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Electrical Engineering (MEE)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["10.25669/q5jv-m9f1","https://oasis.library.unlv.edu/rtds/840","https://oasis.library.unlv.edu/context/rtds/article/1839/viewcontent/uc.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["At present early detection is critical for the cure of breast cancer. Mammography is a breast screening technique which can detect breast cancer at the earliest possible stage. Mammographic lesions are typically classified into three shape classes, namely round, nodular and stellate. Presently this classification is done by experienced radiologists. In order to increase the speed and decrease the cost of diagnosis, automated recognition systems are being developed. This study analyses an automated classification procedure and its sensitivity to wavelet based image compression; In this study, the mammographic shape images are compressed using discrete wavelet compression and then classified using statistical classification methods. First, one dimensional compression is done on the radial distance measure and the shape features are extracted. Second, linear discriminant analysis is used to compute the weightings of the features. Third, a minimum distance Euclidean classifier and the leave-one-out test method is used for classification. Lastly, a two dimensional compression is performed on the images, and the above process of feature extraction and classification is repeated. The results are compared with those obtained with uncompressed mammographic images."]},{"key":"dc:format","label":"Dc Format","values":["pdf"]},{"key":"dc:title","label":"Title","values":["Effects of discrete wavelet compression on automated mammographic shape recognition"]}]}],"canonical_facts":{"dc:contributor":["Lori Bruce"],"dc:creator":["Kalluri, Ravikiran"],"dc:description.abstract":["At present early detection is critical for the cure of breast cancer. Mammography is a breast screening technique which can detect breast cancer at the earliest possible stage. Mammographic lesions are typically classified into three shape classes, namely round, nodular and stellate. Presently this classification is done by experienced radiologists. In order to increase the speed and decrease the cost of diagnosis, automated recognition systems are being developed. This study analyses an automated classification procedure and its sensitivity to wavelet based image compression; In this study, the mammographic shape images are compressed using discrete wavelet compression and then classified using statistical classification methods. First, one dimensional compression is done on the radial distance measure and the shape features are extracted. Second, linear discriminant analysis is used to compute the weightings of the features. Third, a minimum distance Euclidean classifier and the leave-one-out test method is used for classification. Lastly, a two dimensional compression is performed on the images, and the above process of feature extraction and classification is repeated. The results are compared with those obtained with uncompressed mammographic images."],"dc:format":["pdf"],"dc:identifier":["10.25669/q5jv-m9f1","https://oasis.library.unlv.edu/rtds/840","https://oasis.library.unlv.edu/context/rtds/article/1839/viewcontent/uc.pdf"],"dc:publisher":["University of Nevada, Las Vegas"],"dc:rights":["IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/"],"dc:title":["Effects of discrete wavelet compression on automated mammographic shape recognition"],"dc:type":["Text"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Electrical Engineering (MEE)"]},"updated_at":"2026-07-24T05:24:45Z"}