<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<site>sibgrapi.sid.inpe.br 802</site>
		<holdercode>{ibi 8JMKD3MGPEW34M/46T9EHH}</holdercode>
		<identifier>83LX3pFwXQZeBBx/fLCTU</identifier>
		<repository>dpi.inpe.br/banon/1999/01.14.11.43</repository>
		<lastupdate>1999:01.18.02.00.00 sid.inpe.br/banon/2001/03.30.15.38 administrator</lastupdate>
		<metadatarepository>sid.inpe.br/banon/2001/03.30.15.53.59</metadatarepository>
		<metadatalastupdate>2022:06.14.00.16.32 sid.inpe.br/banon/2001/03.30.15.38 administrator {D 1998}</metadatalastupdate>
		<citationkey>SoaresConcVian:1998:AuClMa</citationkey>
		<title>Automated classification of masses on mammography</title>
		<year>1998</year>
		<numberoffiles>1</numberoffiles>
		<size>817 KiB</size>
		<author>Soares, Luciana Marinho,</author>
		<author>Conci, Aura,</author>
		<author>Vianna, Alberto D.,</author>
		<editor>Costa, L. da F,</editor>
		<editor>Camara, G.,</editor>
		<conferencename>International Symposium on Computer Graphics, Image Processing and Vision, 11 (SIBGRAPI)</conferencename>
		<conferencelocation>Rio de Janeiro, RJ, Brazil</conferencelocation>
		<date>20-23 Oct. 1998</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<organization>SBC - Sociedade Brasileira de Computação</organization>
		<transferableflag>1</transferableflag>
		<keywords>biomedical image processing, mammography image database, breast cancer, classification of nodules, mass classifier in mammography, digitized mammograms, processamento de imagens biomedicas, banco de imagens mamograficas, cancer do seio, classificacao de nodulos cancerigenos, classificacao de elementos mamograficos, mamografia digitais</keywords>
		<abstract>A scheme for identification of breast cancer as benignan or malignant based on pattern recognition is presented. A database for use by the mammographic image analysis research community has been established. From these images, 52 cases with undoubted diagnosis have been used as input pattern for feacture extraction and classification training. After extensive experimentation a set of feactures is extracted using shape and contour characterization. Two classes of classifier are used: discriminant functions and nearest neighbor classifier.We implemented an automatic computer dignosis system that performs analysis capable of correct classification on all tested cases until now.</abstract>
		<targetfile>sse086.pdf</targetfile>
		<usergroup>administrator</usergroup>
		<visibility>shown</visibility>
		<nexthigherunit>8JMKD3MGPEW34M/46RGNB8</nexthigherunit>
		<nexthigherunit>8JMKD3MGPEW34M/4742MCS</nexthigherunit>
		<citingitemlist>sid.inpe.br/sibgrapi/2022/05.08.04.49 6</citingitemlist>
		<hostcollection>sid.inpe.br/banon/2001/03.30.15.38</hostcollection>
		<notes>The conference was held in Rio de Janeiro, RJ, Brazil, from October 20 to 23.</notes>
		<lasthostcollection>sid.inpe.br/banon/2001/03.30.15.38</lasthostcollection>
		<url>http://sibgrapi.sid.inpe.br/rep-/dpi.inpe.br/banon/1999/01.14.11.43</url>
	</metadata>
</metadatalist>