untitled
<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd>
<responseDate>2018-01-15T18:31:34Z</responseDate>
<request identifier=oai:HAL:hal-00958856v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request>
<GetRecord>
<record>
<header>
<identifier>oai:HAL:hal-00958856v1</identifier>
<datestamp>2018-01-12</datestamp>
<setSpec>type:COMM</setSpec>
<setSpec>subject:info</setSpec>
<setSpec>collection:CEA</setSpec>
<setSpec>collection:UNICE</setSpec>
<setSpec>collection:CNRS</setSpec>
<setSpec>collection:I3S</setSpec>
<setSpec>collection:BNRMI</setSpec>
<setSpec>collection:UNIV-AG</setSpec>
<setSpec>collection:CEREGMIA</setSpec>
<setSpec>collection:DSV</setSpec>
<setSpec>collection:UCA-TEST</setSpec>
<setSpec>collection:UNIV-COTEDAZUR</setSpec>
</header>
<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>Classification of biological cells using bio-inspired descriptors</title>
<creator>Bel Haj Ali, Wafa</creator>
<creator>Giampaglia, Dario</creator>
<creator>Barlaud, Michel</creator>
<creator>Piro, Paolo</creator>
<creator>Nock, Richard</creator>
<creator>Pourcher, Thierry</creator>
<contributor>Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Projet MEDIACODING ; Signal, Images et Systèmes (SIS) ; Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S) ; Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S) ; Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>Istituto Italiano di Tecnologia (IIT)</contributor>
<contributor>Centre de Recherche en Economie, Gestion, Modélisation et Informatique Appliquée (CEREGMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>Transporteurs en Imagerie et Radiothérapie en Oncologie (TIRO - UMR E93) ; Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Commissariat à l'énergie atomique et aux énergies alternatives (CEA) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<description>International audience</description>
<source>ICPR 2012</source>
<source>ICPR - 21st International Conference on Pattern Recognition</source>
<coverage>Tsukuba, Japan</coverage>
<publisher>IEEE</publisher>
<identifier>hal-00958856</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00958856</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00958856/document</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00958856/file/icpr_2012.pdf</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00958856</source>
<source>ICPR - 21st International Conference on Pattern Recognition, Nov 2012, Tsukuba, Japan. IEEE, pp.3353-3357, 2012</source>
<language>en</language>
<subject lang=en>cellular biophysics</subject>
<subject lang=en>diseases</subject>
<subject lang=en>feature extraction</subject>
<subject lang=en>image classification</subject>
<subject lang=en>learning (artificial intelligence)</subject>
<subject lang=en>medical image processing</subject>
<subject>[INFO.INFO-IM] Computer Science [cs]/Medical Imaging</subject>
<subject>[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]</subject>
<subject>[INFO.INFO-TI] Computer Science [cs]/Image Processing</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>This paper proposes a novel automated approach for the categorization of cells in fluorescence microscopy images. Our supervised classification method aims at recognizing patterns of unlabeled cells based on an annotated dataset. First, the cell images need to be indexed by encoding them in a feature space. For this purpose, we propose tailored bio-inspired features relying on the distribution of contrast information. Then, a supervised learning algorithm is proposed for classifying the cells. We carried out experiments on cellular images related to the diagnosis of autoimmune diseases, testing our classification method on the HEp-2 Cells dataset of Foggia et al (CBMS 2010). Results show classification precision larger than 96% on average, thus confirming promising application of our approach to the challenging application of cellular image classification for computer-aided diagnosis.</description>
<date>2012-11-11</date>
</dc>
</metadata>
</record>
</GetRecord>
</OAI-PMH>
