| Comparison of feature extraction and selection methods in mammogram recognition. | |
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MedLine Citation:
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PMID: 12594085 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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This paper presents a comparison of feature extraction and selection methods in the design of mammogram recognition systems. Mammographic images were classified into two categories, normal and cancerous. The following methods of feature extraction were investigated: two-dimensional Haar wavelets, histograms, and singular value decomposition. The feature patterns were reduced and selected using principal component analysis (PCA) and rough sets. The rough sets methods were applied to the final selection of the pattern features. Classification of mammograms was realized using an error backpropagation neural network. |
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Authors:
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R Swiniarski; A Swiniarska |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: Annals of the New York Academy of Sciences Volume: 980 ISSN: 0077-8923 ISO Abbreviation: Ann. N. Y. Acad. Sci. Publication Date: 2002 Dec |
Date Detail:
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Created Date: 2003-02-20 Completed Date: 2003-03-20 Revised Date: 2006-11-15 |
Medline Journal Info:
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Nlm Unique ID: 7506858 Medline TA: Ann N Y Acad Sci Country: United States |
Other Details:
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Languages: eng Pagination: 116-24 Citation Subset: IM |
Affiliation:
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Department of Computer Science, San Diego State University, California 92182, USA. rswiniar@sciences.sdsu.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Breast
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cytology Breast Neoplasms / radiography Female Humans Mammography / methods* Pattern Recognition, Automated Reference Values |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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