Multimodal Indexing and Information Retrieval in Medical Image Mammographies: Digital Learning Based on Gabor Filters Model
Identifieur interne : 000241 ( Main/Exploration ); précédent : 000240; suivant : 000242Multimodal Indexing and Information Retrieval in Medical Image Mammographies: Digital Learning Based on Gabor Filters Model
Auteurs : Sahbi Sidhom [France] ; Noureddine Bourkache [Algérie] ; Mourad Laghrouche [Algérie]Source :
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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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last="Laghrouche">Mourad Laghrouche</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-253079" status="VALID"><orgName>Laboratoire d'Analyse & Modélisation des Phénomènes Aléatoires [Tizi-Ouzou]</orgName><orgName type="acronym">LAMPA</orgName><desc><address><addrLine>B.P.17 RP, 15000 Tizi-Ouzou</addrLine><country key="DZ"></country></address><ref type="url">http://www.ummto.dz/spip.php?rubrique474</ref></desc><listRelation><relation active="#struct-252490" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-252490" type="direct"><org type="institution" xml:id="struct-252490" status="VALID"><orgName>Université Mouloud Mammeri de Tizi Ouzou [Tizi Ouzou]</orgName><orgName type="acronym">UMMTO</orgName><desc><address><addrLine>15000 Tizi-Ouzou, Algérie</addrLine><country key="DZ"></country></address><ref type="url">http://www.ummto.dz/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>Algérie</country></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term> Gabor Model (digital signature)</term><term> cancer diagnosis</term><term> digital learning process</term><term> image analysis</term><term> indexing system</term><term> information retrieval (IR) system</term><term> mammography databases (MIAS)</term><term> multimodal framework.</term><term> query by image content</term><term> semantic attributes</term><term>medical image scanner</term></keywords></textClass></profileDesc></teiHeader><front><div type="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.</div></front></TEI><affiliations><list><country><li>Algérie</li><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Sidhom, Sahbi" sort="Sidhom, Sahbi" uniqKey="Sidhom S" first="Sahbi" last="Sidhom">Sahbi Sidhom</name></region></country><country name="Algérie"><noRegion><name sortKey="Bourkache, Noureddine" sort="Bourkache, Noureddine" uniqKey="Bourkache N" first="Noureddine" last="Bourkache">Noureddine Bourkache</name></noRegion><name sortKey="Laghrouche, Mourad" sort="Laghrouche, Mourad" uniqKey="Laghrouche M" first="Mourad" last="Laghrouche">Mourad Laghrouche</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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