A Novel Breast Tissue Density Classification Methodology
It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for th...
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| Veröffentlicht in: | IEEE journal of biomedical and health informatics 2008-01, Vol.12 (1), p.55-65 |
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| container_title | IEEE journal of biomedical and health informatics |
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| creator | Oliver, A. Freixenet, J. Marti, R. Pont, J. Perez, E. Denton, E.R.E. Zwiggelaar, R. |
| description | It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment. |
| doi_str_mv | 10.1109/TITB.2007.903514 |
| format | Article |
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In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. 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In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment.</description><subject>Automation</subject><subject>Bayes Theorem</subject><subject>Bayesian methods</subject><subject>Bayesian statistical decision</subject><subject>Breast</subject><subject>Breast - pathology</subject><subject>Breast cancer</subject><subject>Breast density classification</subject><subject>Breast tissue</subject><subject>computer-aided diagnostic systems</subject><subject>Data mining</subject><subject>Data systems</subject><subject>Database Management Systems</subject><subject>Diagnostic imaging</subject><subject>Diagnòstic per la imatge</subject><subject>Digital techniques</subject><subject>Estadística bayesiana</subject><subject>Female</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Image segmentation</subject><subject>Imaging segmentation</subject><subject>Imaging systems in medicine</subject><subject>Imatgeria mèdica</subject><subject>Imatges</subject><subject>Life estimation</subject><subject>Mama</subject><subject>Mammography</subject><subject>parenchymal patterns</subject><subject>Processament</subject><subject>Radiografia</subject><subject>Radiografia mèdica</subject><subject>Radiography</subject><subject>Radiography, Medical</subject><subject>Radiology</subject><subject>Segmentació</subject><subject>Tècniques digitals</subject><issn>1089-7771</issn><issn>2168-2194</issn><issn>1558-0032</issn><issn>2168-2208</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><sourceid>XX2</sourceid><recordid>eNqFks2LFDEQxRtR3HX1LgjS7EFPPVZSSSo57o5fC6texnNIZ9KapWeyJt3C_PdmmMEFD84hJEX9XlG8vKZ5yWDBGJh3q5vV9YID0MIASiYeNedMSt0BIH9c36BNR0TsrHlWyh0AE5Lh0-aMaU6VofNGX7Vf0-8wttc5uDK1q1jKHNr3YVvitGuXoyslDtG7KaZt-yVMP9M6jenH7nnzZHBjCS-O90Xz_eOH1fJzd_vt083y6rbzCtjUkTOeAxpCI7TWpkdico3OuH5wRg6IQLWj-jV5kporj67nnAtljNJmwIuGHeb6Mnubgw-5LmOTiw_F_nAgbpFLBqJq3h409zn9mkOZ7CYWH8bRbUOai61mKZQc-ElSk-ICUOhKvvkvScC10fI0iEKgYkqdBDkoJlBSBS__Ae_SnLfVdasVp-qTMBWCo085lZLDYO9z3Li8swzsPix2Hxa7D4s9hKVKXh_nzv0mrB8Ex3RU4NUBiCGEv-26U_1Iwj9jcL4z</recordid><startdate>200801</startdate><enddate>200801</enddate><creator>Oliver, A.</creator><creator>Freixenet, J.</creator><creator>Marti, R.</creator><creator>Pont, J.</creator><creator>Perez, E.</creator><creator>Denton, E.R.E.</creator><creator>Zwiggelaar, R.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Automation Bayes Theorem Bayesian methods Bayesian statistical decision Breast Breast - pathology Breast cancer Breast density classification Breast tissue computer-aided diagnostic systems Data mining Data systems Database Management Systems Diagnostic imaging Diagnòstic per la imatge Digital techniques Estadística bayesiana Female Hospitals Humans Image segmentation Imaging segmentation Imaging systems in medicine Imatgeria mèdica Imatges Life estimation Mama Mammography parenchymal patterns Processament Radiografia Radiografia mèdica Radiography Radiography, Medical Radiology Segmentació Tècniques digitals |
| title | A Novel Breast Tissue Density Classification Methodology |
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