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020 _a9783540278160
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024 7 _a10.1007/b98995
_2doi
050 4 _aTA1634
072 7 _aUYQV
_2bicssc
072 7 _aCOM016000
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072 7 _aUYQV
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082 0 4 _a006.37
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245 1 0 _aComputer Vision and Mathematical Methods in Medical and Biomedical Image Analysis
_h[electronic resource] :
_bECCV 2004 Workshops CVAMIA and MMBIA Prague, Czech Republic, May 15, 2004, Revised Selected Papers /
_cedited by Milan Sonka, Ioannis A. Kakadiaris, Jan Kybic.
250 _a1st ed. 2004.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2004.
300 _aXII, 444 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v3117
505 0 _aAcquisition Techniques -- Ultrasound Stimulated Vibro-acoustography -- CT from an Unmodified Standard Fluoroscopy Machine Using a Non-reproducible Path -- Three-Dimensional Object Reconstruction from Compton Scattered Gamma-Ray Data -- Reconstruction -- Cone-Beam Image Reconstruction by Moving Frames -- AQUATICS Reconstruction Software: The Design of a Diagnostic Tool Based on Computer Vision Algorithms -- Towards Automatic Selection of the Regularization Parameters in Emission Tomgraphy by Fourier Synthesis -- Mathematical Methods -- Extraction of Myocardial Contractility Patterns from Short-Axes MR Images Using Independent Component Analysis -- Principal Geodesic Analysis on Symmetric Spaces: Statistics of Diffusion Tensors -- Symmetric Geodesic Shape Averaging and Shape Interpolation -- Smoothing Impulsive Noise Using Nonlinear Diffusion Filtering -- Level Set and Region Based Surface Propagation for Diffusion Tensor MRI Segmentation -- The Beltrami Flow over Triangulated Manifolds -- Hierarchical Analysis of Low-Contrast Temporal Images with Linear Scale Space -- Medical Image Segmentation -- Segmentation of Medical Images with a Shape and Motion Model: A Bayesian Perspective -- A Multi-scale Geometric Flow for Segmenting Vasculature in MRI -- A 2D Fourier Approach to Deformable Model Segmentation of 3D Medical Images -- Automatic Rib Segmentation in CT Data -- Efficient Initialization for Constrained Active Surfaces, Applications in 3D Medical Images -- An Information Fusion Method for the Automatic Delineation of the Bone-Soft Tissues Interface in Ultrasound Images -- Multi-label Image Segmentation for Medical Applications Based on Graph-Theoretic Electrical Potentials -- Three-Dimensional Mass Reconstruction in Mammography -- Segmentation of Abdominal Aortic Aneurysms with a Non-parametric Appearance Model -- Probabilistic Spatial-Temporal Segmentation of Multiple Sclerosis Lesions -- Segmenting Cell Images: A Deterministic Relaxation Approach -- Registration -- TIGER – A New Model for Spatio-temporal Realignment of FMRI Data -- Robust Registration of 3-D Ultrasound Images Based on Gabor Filter and Mean-Shift Method -- Deformable Image Registration by Adaptive Gaussian Forces -- Applications -- Statistical Imaging for Modeling and Identification of Bacterial Types -- Assessment of Intrathoracic Airway Trees: Methods and In Vivo Validation -- Computer-Aided Measurement of Solid Breast Tumor Features on Ultrasound Images -- Can a Continuity Heuristic Be Used to Resolve the Inclination Ambiguity of Polarized Light Imaging? -- Applications of Image Registration in Human Genome Research -- Fast Marching 3D Reconstruction of Interphase Chromosomes -- Robust Extraction of the Optic Nerve Head in Optical Coherence Tomography -- Scale-Space Diagnostic Criterion for Microscopic Image Analysis -- Image Registration Neural System for the Analysis of Fundus Topology -- Robust Identification of Object Elasticity.
520 _aMedical imaging and medical image analysisare rapidly developing. While m- ical imaging has already become a standard of modern medical care, medical image analysis is still mostly performed visually and qualitatively. The ev- increasing volume of acquired data makes it impossible to utilize them in full. Equally important, the visual approaches to medical image analysis are known to su?er from a lack of reproducibility. A signi?cant researche?ort is devoted to developing algorithms for processing the wealth of data available and extracting the relevant information in a computerized and quantitative fashion. Medical imaging and image analysis are interdisciplinary areas combining electrical, computer, and biomedical engineering; computer science; mathem- ics; physics; statistics; biology; medicine; and other ?elds. Medical imaging and computer vision, interestingly enough, have developed and continue developing somewhat independently. Nevertheless, bringing them together promises to b- e?t both of these ?elds. We were enthusiastic when the organizers of the 2004 European Conference on Computer Vision (ECCV) allowed us to organize a satellite workshop devoted to medical image analysis.
650 0 _aComputer vision.
650 0 _aComputer industry.
650 0 _aArtificial intelligence.
650 0 _aPattern recognition systems.
650 0 _aComputer graphics.
650 0 _aMedical informatics.
650 1 4 _aComputer Vision.
650 2 4 _aThe Computer Industry.
650 2 4 _aArtificial Intelligence.
650 2 4 _aAutomated Pattern Recognition.
650 2 4 _aComputer Graphics.
650 2 4 _aHealth Informatics.
700 1 _aSonka, Milan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aKakadiaris, Ioannis A.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aKybic, Jan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9783662191941
830 0 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v3117
856 4 0 _uhttps://doi.org/10.1007/b98995
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