Multimodal Indexing and Information Retrieval in Medical Image Mammographies: Digital Learning Based on Gabor Filters Model
Identifieur interne : 003449 ( Hal/Corpus ); précédent : 003448; suivant : 003450Multimodal Indexing and Information Retrieval in Medical Image Mammographies: Digital Learning Based on Gabor Filters Model
Auteurs : Sahbi Sidhom ; Noureddine Bourkache ; Mourad LaghroucheSource :
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Abstract
In this chapter, we propose a new indexing approach on medical “image scanner” databases combiningthe analysis process of the texture characteristics with the information contents. The proposed modelis based on the digital image components using the vector of characteristics. This vector represent themorphological processing result on image texture. It is linked to semantic attributes of the image usingthe annotations of medical professionals. Our context of study is based on “Mammographic ImageAnalysis” (MIAS) in databases. The first aspect concerning the morphology processing on images calledthe “numerical signature” vector. In our approach, the image analysis of the texture is based on theGabor Wavelets (or Filters) Theory. In offline processing for each image in MIAS databases, the GaborWavelets determine all numerical signatures: vectors of image characteristics as multi-index. In online,the query by image is in real-time processing to define the query signature (or image-query vectors)and to determine similarities by matching of multi-index with all images in databases. The similaritiesare built between the image-query and images in MIAS databases using the same Gabors’ algorithmsimplemented. In order to evaluate the robustness of our system (based on multi-index, semantic attributes,query and information retrieval by image), we experiment with a controlled database of 320mammographies. The performance results show a set of successful criteria in image representationsbased on the Gabor’s Wavelets, semantic attributes and combining with significant ratios in the systemrecall and precision.
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The proposed modelis based on the digital image components using the vector of characteristics. This vector represent themorphological processing result on image texture. It is linked to semantic attributes of the image usingthe annotations of medical professionals. Our context of study is based on “Mammographic ImageAnalysis” (MIAS) in databases. The first aspect concerning the morphology processing on images calledthe “numerical signature” vector. In our approach, the image analysis of the texture is based on theGabor Wavelets (or Filters) Theory. In offline processing for each image in MIAS databases, the GaborWavelets determine all numerical signatures: vectors of image characteristics as multi-index. In online,the query by image is in real-time processing to define the query signature (or image-query vectors)and to determine similarities by matching of multi-index with all images in databases. The similaritiesare built between the image-query and images in MIAS databases using the same Gabors’ algorithmsimplemented. In order to evaluate the robustness of our system (based on multi-index, semantic attributes,query and information retrieval by image), we experiment with a controlled database of 320mammographies. The performance results show a set of successful criteria in image representationsbased on the Gabor’s Wavelets, semantic attributes and combining with significant ratios in the systemrecall and precision.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Multimodal Indexing and Information Retrieval in Medical Image Mammographies: Digital Learning Based on Gabor Filters Model</title><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">Noureddine</forename><surname>Bourkache</surname></persName><email></email><idno type="halauthor">1120477</idno><affiliation ref="#struct-253079"></affiliation></author><author role="aut"><persName><forename type="first">Mourad</forename><surname>Laghrouche</surname></persName><email></email><idno type="halauthor">1120478</idno><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">2016-01-13 16:57:57</date><date type="whenModified">2016-02-02 13:55:09</date><date type="whenReleased">2016-01-13 16:57:57</date><date type="whenProduced">2015-11-06</date></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-01255457</idno><idno type="halUri">https://hal.inria.fr/hal-01255457</idno><idno type="halBibtex">sidhom:hal-01255457</idno><idno type="halRefHtml">Wahiba Ben Abdessalem Karâa (Taif University, Saudi Arabia & RIADI-GDL Laboratory, ENSI, Tunisia) and Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India). Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes, 1, IGI Global, pp.414, 2015, Advances in Bioinformatics and Biomedical Engineering (ABBEà Book Series, ISBN13: 9781466688117|ISBN10: 1466688114|EISBN13: 9781466688124. <http://www.igi-global.com/book/biomedical-image-analysis-mining-techniques/129595></idno><idno type="halRef">Wahiba Ben Abdessalem Karâa (Taif University, Saudi Arabia & RIADI-GDL Laboratory, ENSI, Tunisia) and Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India). Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes, 1, IGI Global, pp.414, 2015, Advances in Bioinformatics and Biomedical Engineering (ABBEà Book Series, ISBN13: 9781466688117|ISBN10: 1466688114|EISBN13: 9781466688124. <http://www.igi-global.com/book/biomedical-image-analysis-mining-techniques/129595></idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="popular" n="0">No</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Multimodal Indexing and Information Retrieval in Medical Image Mammographies: Digital Learning Based on Gabor Filters Model</title><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">Noureddine</forename><surname>Bourkache</surname></persName><idno type="halAuthorId">1120477</idno><affiliation ref="#struct-253079"></affiliation></author><author role="aut"><persName><forename type="first">Mourad</forename><surname>Laghrouche</surname></persName><idno type="halAuthorId">1120478</idno><affiliation ref="#struct-253079"></affiliation></author></analytic><monogr><idno type="isbn">ISBN13: 9781466688117|ISBN10: 1466688114|EISBN13: 9781466688124</idno><title level="m">Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes</title><editor>Wahiba Ben Abdessalem Karâa (Taif University, Saudi Arabia & RIADI-GDL Laboratory, ENSI, Tunisia) and Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India)</editor><imprint><publisher>IGI Global</publisher><biblScope unit="serie">Advances in Bioinformatics and Biomedical Engineering (ABBEà Book Series</biblScope><biblScope unit="volume">1</biblScope><biblScope unit="pp">414</biblScope><date type="datePub">2015-11-06</date></imprint></monogr><ref type="publisher">http://www.igi-global.com/book/biomedical-image-analysis-mining-techniques/129595</ref></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">medical image scanner</term><term xml:lang="en"> mammography databases (MIAS)</term><term xml:lang="en"> cancer diagnosis</term><term xml:lang="en"> image analysis</term><term xml:lang="en"> Gabor Model (digital signature)</term><term xml:lang="en"> semantic attributes</term><term xml:lang="en"> indexing system</term><term xml:lang="en"> information retrieval (IR) system</term><term xml:lang="en"> query by image content</term><term xml:lang="en"> digital learning process</term><term xml:lang="en"> multimodal framework.</term></keywords><classCode scheme="halDomain" n="info.info-ti">Computer Science [cs]/Image Processing</classCode><classCode scheme="halDomain" n="info.info-ir">Computer Science [cs]/Information Retrieval [cs.IR]</classCode><classCode scheme="halDomain" n="info.info-tt">Computer Science [cs]/Document and Text Processing</classCode><classCode scheme="halTypology" n="COUV">Book section</classCode></textClass><abstract xml:lang="en">In this chapter, we propose a new indexing approach on medical “image scanner” databases combiningthe analysis process of the texture characteristics with the information contents. The proposed modelis based on the digital image components using the vector of characteristics. This vector represent themorphological processing result on image texture. It is linked to semantic attributes of the image usingthe annotations of medical professionals. Our context of study is based on “Mammographic ImageAnalysis” (MIAS) in databases. The first aspect concerning the morphology processing on images calledthe “numerical signature” vector. In our approach, the image analysis of the texture is based on theGabor Wavelets (or Filters) Theory. In offline processing for each image in MIAS databases, the GaborWavelets determine all numerical signatures: vectors of image characteristics as multi-index. In online,the query by image is in real-time processing to define the query signature (or image-query vectors)and to determine similarities by matching of multi-index with all images in databases. The similaritiesare built between the image-query and images in MIAS databases using the same Gabors’ algorithmsimplemented. In order to evaluate the robustness of our system (based on multi-index, semantic attributes,query and information retrieval by image), we experiment with a controlled database of 320mammographies. The performance results show a set of successful criteria in image representationsbased on the Gabor’s Wavelets, semantic attributes and combining with significant ratios in the systemrecall and precision.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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