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Record Nr. |
UNINA9910144179103321 |
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Titolo |
Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis : ECCV 2004 Workshops CVAMIA and MMBIA Prague, Czech Republic, May 15, 2004, Revised Selected Papers / / edited by Milan Sonka, Ioannis A. Kakadiaris, Jan Kybic |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2004 |
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ISBN |
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3-540-27816-8 |
3-540-22675-3 |
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Edizione |
[1st ed. 2004.] |
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Descrizione fisica |
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1 online resource (XII, 444 p.) |
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Collana |
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Lecture Notes in Computer Science, , 0302-9743 ; ; 3117 |
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Disciplina |
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Soggetti |
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Optical data processing |
Computer industry |
Artificial intelligence |
Pattern perception |
Computer graphics |
Medical informatics |
Image Processing and Computer Vision |
The Computer Industry |
Artificial Intelligence |
Pattern Recognition |
Computer Graphics |
Health Informatics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Acquisition 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 |
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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. |
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Sommario/riassunto |
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Medical 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. |
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