LEADER 05751nam 22008055 450 001 996465552403316 005 20200703163853.0 010 $a3-319-47118-X 024 7 $a10.1007/978-3-319-47118-1 035 $a(CKB)3710000000893639 035 $a(DE-He213)978-3-319-47118-1 035 $a(MiAaPQ)EBC6294798 035 $a(MiAaPQ)EBC5590658 035 $a(Au-PeEL)EBL5590658 035 $a(OCoLC)960694766 035 $a(PPN)196323207 035 $a(EXLCZ)993710000000893639 100 $a20160921d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPatch-Based Techniques in Medical Imaging$b[electronic resource] $eSecond International Workshop, Patch-MI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings /$fedited by Guorong Wu, Pierrick Coupé, Yiqiang Zhan, Brent C. Munsell, Daniel Rueckert 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (X, 141 p. 45 illus.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v9993 311 $a3-319-47117-1 320 $aIncludes bibliographical references and index. 327 $aAutomatic Segmentation of Hippocampus for Longitudinal Infant Brain MR Image Sequence by Spatial-Temporal Hypergraph Learning -- Construction of Neonatal Diffusion Atlases via Spatio-Angular Consistency -- Selective Labeling: identifying representative sub-volumes for interactive segmentation -- Robust and Accurate Appearance Models based on Joint Dictionary Learning: Data from the Osteoarthritis Initiative -- Consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation -- Sparse-Based Morphometry: Principle and Application to Alzheimer?s Disease -- Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning -- Patch-Based Discrete Registration of Clinical Brain Images -- Non-local MRI Library-based Super-resolution: Application to Hippocampus Subfield Segmentation -- Patch-based DTI grading: Application to Alzheimer's disease classification -- Hierarchical Multi-Atlas Segmentation using Label-Specific Embeddings, Target-Specific Templates and Patch Refinement -- HIST: HyperIntensity Segmentation Tool -- Supervoxel-Based Hierarchical Markov Random Field Framework for Multi-Atlas Segmentation -- CapAIBL: Automated reporting of cortical PET quantification without need of MRI on brain surface using a patch-based method -- High resolution hippocampus subfield segmentation using multispectral multi-atlas patch-based label fusion -- Identification of water and fat images in Dixon MRI using aggregated patch-based convolutional neural networks -- Estimating Lung Respiratory Motion Using Combined Global and Local Statistical Models. 330 $aThis book constitutes the refereed proceedings of the Second International Workshop on Patch-Based Techniques in Medical Images, Patch-MI 2016, which was held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 17 regular papers presented in this volume were carefully reviewed and selected from 25 submissions. The main aim of the Patch-MI 2016 workshop is to promote methodological advances within the medical imaging field, with various applications in image segmentation, image denoising, image super-resolution, computer-aided diagnosis, image registration, abnormality detection, and image synthesis. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v9993 606 $aOptical data processing 606 $aPattern recognition 606 $aComputer graphics 606 $aArtificial intelligence 606 $aComputer simulation 606 $aAlgorithms 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aComputer Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22013 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 615 0$aOptical data processing. 615 0$aPattern recognition. 615 0$aComputer graphics. 615 0$aArtificial intelligence. 615 0$aComputer simulation. 615 0$aAlgorithms. 615 14$aImage Processing and Computer Vision. 615 24$aPattern Recognition. 615 24$aComputer Graphics. 615 24$aArtificial Intelligence. 615 24$aSimulation and Modeling. 615 24$aAlgorithm Analysis and Problem Complexity. 676 $a004 702 $aWu$b Guorong$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCoupé$b Pierrick$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZhan$b Yiqiang$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMunsell$b Brent C$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRueckert$b Daniel$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465552403316 996 $aPatch-Based Techniques in Medical Imaging$91918861 997 $aUNISA LEADER 01059nam0 22002771i 450 001 VAN0021828 005 20050228120000.0 100 $a20040818d1983 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 200 1 $aAnalisi demografica$econcetti, metodi, risultati$fRoland Pressat$gprefazione di Alfred Sauvy 210 $aMilano$cETAS$d1983 215 $a318 p.$d23 cm. 500 1$3VAN0078404$aˆL' ‰analyse demographique$913801 606 $aDemografia$3VANC010465$2FI 620 $dMilano$3VANL000284 676 $a312$v21 700 1$aPressat$bRoland$3VANV018120$033305 712 $aEtas $3VANV108308$4650 801 $aIT$bSOL$c20230616$gRICA 899 $aBIBLIOTECA DEL DIPARTIMENTO DI ARCHITETTURA E DISEGNO INDUSTRIALE$1IT-CE0107$2VAN01 912 $aVAN0021828 950 $aBIBLIOTECA DEL DIPARTIMENTO DI ARCHITETTURA E DISEGNO INDUSTRIALE$d01PREST IIIAb59 $e01 28833 20051005 996 $aAnalyse demographique$913801 997 $aUNICAMPANIA