Apprentissage numérique pour la recherche d'informations en imagerie médicale : Modélisation des filtres de Gabor
Identifieur interne : 005686 ( Hal/Curation ); précédent : 005685; suivant : 005687Apprentissage numérique pour la recherche d'informations en imagerie médicale : Modélisation des filtres de Gabor
Auteurs : Noureddine Bourkache [Algérie] ; Sahbi Sidhom [France] ; Mourad Laghrouche [Algérie]Source :
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Abstract
We propose in this paper , a method of indexing and searching images by exploiting the digital image content . Our method is based on the representation of digital image content by a specific vector to index image characteristics. This vector will be called : digital signature of the image. To do this, we exploited the texture of the image using Gabor wavelets . In this work , each image of the training set is represented by its indexed and characteristics ( texture ) . This representation takes place offline , is characterized by the backing , in a database , all the signatures of indexed images . This allows us, online, to search digital similarity to a query picture. This same query image is indexed online with the same algorithm used offline . To evaluate the performance we tested our application on a learning image database containing 320 mammograms. The results show that the representation of the digital image content is important in information retrieval imaging .
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exploiting the digital image content . Our method is based on the representation of digital image content by a specific vector to index image characteristics. This vector will be called : digital signature of the image. To do this, we exploited the texture of the image using Gabor wavelets . In this work , each image of the training set is represented by its indexed and characteristics ( texture ) . This representation takes place offline , is characterized by the backing , in a database , all the signatures of indexed images . This allows us, online, to search digital similarity to a query picture. This same query image is indexed online with the same algorithm used offline . To evaluate the performance we tested our application on a learning image database containing 320 mammograms. The results show that the representation of the digital image content is important in information retrieval imaging .</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="fr">Apprentissage numérique pour la recherche d'informations en imagerie médicale : Modélisation des filtres de Gabor</title><author role="aut"><persName><forename type="first">Noureddine</forename><surname>Bourkache</surname></persName><email></email><idno type="halauthor">965323</idno><orgName ref="#struct-377018"></orgName><affiliation ref="#struct-397298"></affiliation></author><author role="aut"><persName><forename type="first">Sahbi</forename><surname>Sidhom</surname></persName><email>Sahbi.Sidhom@loria.fr</email><idno type="halauthor">59094</idno><affiliation ref="#struct-206039"></affiliation></author><author role="aut"><persName><forename type="first">Mourad</forename><surname>Laghrouche</surname></persName><email></email><idno type="halauthor">965324</idno><orgName ref="#struct-377018"></orgName><affiliation ref="#struct-253079"></affiliation></author><editor role="depositor"><persName><forename>Sahbi</forename><surname>Sidhom</surname></persName><email>Sahbi.Sidhom@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2014-01-13 15:44:16</date><date type="whenWritten">2013-10-08</date><date type="whenModified">2015-09-21 11:14:36</date><date type="whenReleased">2014-01-14 12:49:59</date><date type="whenProduced">2013-11-08</date><date type="whenEndEmbargoed">2014-01-13</date><ref type="file" target="https://hal.inria.fr/hal-00927280/document"><date notBefore="2014-01-13"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/hal-00927280/file/27.pdf"><date notBefore="2014-01-13"></date></ref></edition><respStmt><resp>contributor</resp><name key="103177"><persName><forename>Sahbi</forename><surname>Sidhom</surname></persName><email>Sahbi.Sidhom@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-00927280</idno><idno type="halUri">https://hal.inria.fr/hal-00927280</idno><idno type="halBibtex">bourkache:hal-00927280</idno><idno type="halRefHtml">ENSIAS - Université Mohammed V Souissi, Rabat, Maroc. International symposium ISKO-Maghreb ̓ 2013 "Concepts and Tools for Knowledge Management (KM)", Nov 2013, Marrakech, Maroc. 1 (ISBN : 978-1-4799-3391-4 / Part Number : CFP1316X-CDR), 2013, ISKO-Maghreb'2013; "Concepts and Tools for Knowledge Management (KM)"</idno><idno type="halRef">ENSIAS - Université Mohammed V Souissi, Rabat, Maroc. International symposium ISKO-Maghreb ̓ 2013 "Concepts and Tools for Knowledge Management (KM)", Nov 2013, Marrakech, Maroc. 1 (ISBN : 978-1-4799-3391-4 / Part Number : CFP1316X-CDR), 2013, ISKO-Maghreb'2013; "Concepts and Tools for Knowledge Management (KM)"</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="commentary">ISBN : 978-1-4799-3391-4 / Part Number : CFP1316X-CDR</note><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="fr">Apprentissage numérique pour la recherche d'informations en imagerie médicale : Modélisation des filtres de Gabor</title><author role="aut"><persName><forename type="first">Noureddine</forename><surname>Bourkache</surname></persName><idno type="halAuthorId">965323</idno><orgName ref="#struct-377018"></orgName><affiliation ref="#struct-397298"></affiliation></author><author role="aut"><persName><forename type="first">Sahbi</forename><surname>Sidhom</surname></persName><email>Sahbi.Sidhom@loria.fr</email><idno type="halAuthorId">59094</idno><affiliation ref="#struct-206039"></affiliation></author><author role="aut"><persName><forename type="first">Mourad</forename><surname>Laghrouche</surname></persName><idno type="halAuthorId">965324</idno><orgName ref="#struct-377018"></orgName><affiliation ref="#struct-253079"></affiliation></author></analytic><monogr><meeting><title>International symposium ISKO-Maghreb ̓ 2013 "Concepts and Tools for Knowledge Management (KM)"</title><date type="start">2013-11-08</date><date type="end">2013-11-09</date><settlement>Marrakech</settlement><country key="MA">Morocco</country></meeting><respStmt><resp>conferenceOrganizer</resp><name>ENSIAS - Université Mohammed V Souissi, Rabat, Maroc</name></respStmt><editor>ENSIAS - Université Mohammed V Souissi, Rabat, Maroc</editor><imprint><biblScope unit="serie"></biblScope><biblScope unit="volume">1 (ISBN : 978-1-4799-3391-4 / Part Number : CFP1316X-CDR)</biblScope><date type="datePub">2013-11-08</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="fr">French</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Indexing of medical images</term><term xml:lang="en">search images by content</term><term xml:lang="en">digital signature</term><term xml:lang="en">Gabor filter</term><term xml:lang="en">mammography</term><term xml:lang="en">digital learning</term></keywords><classCode scheme="acm" n="H.4"></classCode><classCode scheme="halDomain" n="info.info-ti">Computer Science [cs]/Image Processing</classCode><classCode scheme="halDomain" n="info.info-na">Computer Science [cs]/Numerical Analysis [cs.NA]</classCode><classCode scheme="halDomain" n="info.info-ir">Computer Science [cs]/Information Retrieval [cs.IR]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">We propose in this paper , a method of indexing and searching images by exploiting the digital image content . Our method is based on the representation of digital image content by a specific vector to index image characteristics. This vector will be called : digital signature of the image. To do this, we exploited the texture of the image using Gabor wavelets . In this work , each image of the training set is represented by its indexed and characteristics ( texture ) . This representation takes place offline , is characterized by the backing , in a database , all the signatures of indexed images . This allows us, online, to search digital similarity to a query picture. This same query image is indexed online with the same algorithm used offline . To evaluate the performance we tested our application on a learning image database containing 320 mammograms. The results show that the representation of the digital image content is important in information retrieval imaging .</abstract><abstract xml:lang="fr">Nous proposons, dans cet article, une méthode d'indexation et de recherche d'images en exploitant le contenu numérique des images. Notre méthode est fondée sur la représentation du contenu numérique de l'image par un vecteur de caractéristiques propres à l'image indexée. Ce vecteur sera appelé : signature numérique de l'image. Pour ce faire, nous avons exploité la texture de l'image en utilisant les ondelettes de Gabor. Dans ce travail, chaque image de la base d'apprentissage est indexée et représentée par ses caractéristiques (texture). Cette représentation, qui s'effectue en offline, est caractérisée par la sauvegarde, dans une base de données, de toutes les signatures des images indexées. Ce qui nous permet, en online, d'effectuer une recherche de similarité numérique par rapport à une image requête. Cette même image requête sera indexée en online avec le même algorithme utilisé en offline. Afin d'évaluer les performances nous avons testé notre application sur une base d'images d'apprentissage contenant 320 mammographies. Les résultats obtenus montrent bien que la représentation du contenu numérique des images s'avère important en matière de recherche d'information en imagerie.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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