Kooperativer Bibliotheksverbund

UID:

Format: XIX, 555 S. : Ill., graph. Darst.

Edition: 1. ed., reprinted

ISBN: 0-412-45570-6

Series Statement: Chapman & Hall computing series

Note: Literaturverz. S. 534 - 542

Language: English

Subjects: Psychology

Keywords: Bildverarbeitung ; Maschinelles Sehen ; Bildverarbeitung ; Mathematische Methode

UID:

Format: XXXV, 870 S. , Ill., graph. Darst.

Edition: 4. ed., international edition

ISBN: 1133593690 , 9781133593690

Language: English

Subjects: Computer Science

Keywords: Bildverarbeitung ; Maschinelles Sehen ; Bildverarbeitung ; Mathematische Methode

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Format: XII, 438 S. , Ill., graph. Darst. , 235 mm x 155 mm

ISBN: 3540226753

Series Statement: Lecture notes in computer science 3117

Content: This book constitutes the thoroughly refereed joint post proceedings of the international workshop Computer Vision Approaches to Medical Image Analysis, CVAMIA 2004 and Mathematical Methods in Biomedical Image Analysis, MMBIA 2004, both held in Prague, Czech Republic in May 2004 as part of EECV 2004. The 37 revised full papers presented were carefully selected and improved during two rounds of reviewing and recvsion. The papers are organized in topical sections on image acquision techniques, image reconstruction, mathematical methods, medical image segmentation, image registration, and applications

Additional Edition: Online-Ausg. Sonka, Milan Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis Berlin, Heidelberg : Springer Berlin Heidelberg, 2004 ISBN 9783540226758

Additional Edition: Erscheint auch als Online-Ausgabe Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis Berlin : Springer, 2004 ISBN 9783540278160

Language: English

Subjects: Computer Science

Keywords: Medizin ; Maschinelles Sehen ; Bildanalyse ; Konferenzschrift ; Kongress ; Konferenzschrift

URL: Einführung/Vorwort

URL: Cover

URL: Inhaltsverzeichnis

URL: Volltext

URL: http://www.loc.gov/catdir/enhancements/fy0814/2004111941-d.html

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Format: XXIV, 770 S , Ill., graph. Darst

Edition: 2. ed.

ISBN: 053495393X

Note: Previous ed.: 1993 , Includes bibliographical references and index

Language: English

Subjects: Computer Science

Keywords: Bildverarbeitung ; Maschinelles Sehen ; Bildverarbeitung ; Maschinelles Sehen

UID:

Format: XII, 438 S. : , Ill., graph. Darst.

ISBN: 3-540-22675-3

Series Statement: Lecture notes in computer science 3117

Language: English

UID:

Format: 1218 S.

ISBN: 0-8194-3622-4

Series Statement: Handbook of medical imaging Vol. 2

UID:

Format: XIX, 555 S. , Ill., zahlr. graph. Darst.

Edition: 1. ed.

ISBN: 0412455706

Series Statement: Chapman & Hall computing series

Language: English

Subjects: Psychology

Keywords: Bildverarbeitung ; Maschinelles Sehen ; Bildverarbeitung ; Mathematische Methode ; Bildverarbeitung

UID:

Edition: 3. ed.

ISBN: 9780495082521 , 049508252X

Language: Undetermined

Subjects: Psychology

Keywords: Bildverarbeitung ; Mathematische Methode ; Bildverarbeitung ; Maschinelles Sehen

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Format: Online-Ressource (XII, 438 p. Also available online) , digital

Edition: Springer eBook Collection. Computer Science

ISBN: 9783540278160

Series Statement: Lecture Notes in Computer Science 3117

Content: This book constitutes the thoroughly refereed joint post proceedings of the international workshop Computer Vision Approaches to Medical Image Analysis, CVAMIA 2004, and Mathematical Methods in Biomedical Image Analysis, MMBIA 2004, both held in Prague, Czech Republic, in May 2004 as part of EECV 2004. The 37 revised full papers presented were carefully selected and improved during two rounds of reviewing and revision. The papers are organized in topical sections on image acquision techniques, image reconstruction, mathematical methods, medical image segmentation, image registration, and applications

Additional Edition: ISBN 9783540226758

Additional Edition: Erscheint auch als Druck-Ausgabe Computer vision and mathematical methods in medical and biomedical image analysis Berlin : Springer, 2004 ISBN 3540226753

Language: English

Subjects: Computer Science

Keywords: Medizin ; Maschinelles Sehen ; Bildanalyse ; Konferenzschrift

DOI: 10.1007/b98995

URL: Volltext  (lizenzpflichtig)

URL: lizenzpflichtig

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Format: 1 online resource (700 pages)

Edition: First edition.

ISBN: 0-12-813658-8

Series Statement: The MICCAI Society Book Series

Note: Front Cover -- Medical Image Analysis -- Copyright -- Section editors -- Contents -- Editors -- Contributors -- Preface -- Nomenclature -- Acknowledgments -- Part I Introductory topics -- 1 Medical imaging modalities -- 1.1 Introduction -- 1.2 Image quality -- 1.2.1 Resolution and noise -- 1.2.2 Comparing image appearance -- 1.2.3 Task-based assessment -- 1.3 Modalities and contrast mechanisms -- 1.3.1 X-ray transmission imaging -- 1.3.2 Molecular imaging -- 1.3.3 Optical imaging -- 1.3.4 Large wavelengths and transversal waves -- 1.3.5 A historical perspective on medical imaging -- 1.3.6 Simulating image formation -- 1.4 Clinical scenarios -- 1.4.1 Stroke -- 1.4.2 Oncology -- 1.4.3 Osteonecrosis -- 1.5 Exercises -- References -- 2 Mathematical preliminaries -- 2.1 Introduction -- 2.2 Imaging: definitions, quality and similarity measures -- 2.3 Vector and matrix theory results -- 2.3.1 General concepts -- 2.3.2 Eigenanalysis -- 2.3.3 Singular value decomposition -- 2.3.4 Matrix exponential -- 2.3.4.1 Generalities -- 2.3.4.2 An example which gives rise to a matrix exponential -- 2.4 Linear processing and transformed domains -- 2.4.1 Linear processing. Convolution -- 2.4.2 Transformed domains -- 2.4.2.1 1D Fourier transform -- 2.4.2.2 2D Fourier transform -- 2.4.2.3 N-dimensional Fourier transform -- 2.5 Calculus -- 2.5.1 Derivatives, gradients, and Laplacians -- 2.5.2 Calculus of variations -- 2.5.3 Some specific cases -- 2.5.3.1 Laplace equation -- 2.5.3.2 Heat (or diffusion) equation -- 2.5.4 Leibniz rule for interchanging integrals and derivatives -- 2.6 Notions on shapes -- 2.6.1 Procrustes matching between two planar shapes -- 2.6.2 Mean shape -- 2.6.3 Procrustes analysis in higher dimensions -- 2.7 Exercises -- References -- 3 Regression and classification -- 3.1 Introduction -- Nomenclature -- 3.1.1 Regression as a minimization problem. , 3.1.2 Regression from the statistical angle -- 3.2 Multidimensional linear regression -- 3.2.1 Direction of prediction -- 3.2.2 Risk minimization -- 3.2.2.1 Gauss-Markov theorem -- 3.2.3 Measures of fitting and prediction quality -- 3.2.4 Out-of-sample performance: cross-validation methods -- 3.2.5 Shrinkage methods -- 3.2.5.1 Dealing with multicollinearity: ridge regression -- 3.2.5.2 Dealing with sparsity: the LASSO -- 3.2.5.3 Other general regularizers -- 3.3 Treating non-linear problems with linear models -- 3.3.1 Generalized linear models: transforming y -- 3.3.1.1 Classification as a regression problem: logistic regression -- 3.3.2 Feature spaces: transforming X -- 3.3.2.1 Categorical variables -- 3.3.2.2 Linearizing non-linear regression: functional bases -- 3.3.3 Going further -- 3.4 Exercises -- References -- 4 Estimation and inference -- 4.1 Introduction: what is estimation? -- 4.2 Sampling distributions -- 4.2.1 Cumulative distribution function -- 4.2.2 The Kolmogorov-Smirnov test -- 4.2.3 Histogram as probability density function estimate -- 4.2.4 The chi-squared test -- 4.3 Estimation. Data-based methods -- 4.3.1 Definition of estimator and criteria for design and performance measurement -- 4.3.2 A benchmark for unbiased estimators: the Cramer-Rao lower bound -- 4.3.3 Maximum likelihood estimator -- 4.3.4 The expectation-maximization method -- 4.4 A working example -- 4.5 Estimation. Bayesian methods -- 4.5.1 Definition of Bayesian estimator and design criteria -- 4.5.2 Design criteria for Bayesian estimators -- 4.5.3 Performance measurement -- 4.5.4 The Gaussian case -- 4.5.5 Conjugate distribution and conjugate priors -- 4.5.6 A working example -- 4.6 Monte Carlo methods -- 4.6.1 A non-stochastic use of Monte Carlo -- 4.7 Exercises -- References -- Part II Image representation and processing. , 5 Image representation and 2D signal processing -- 5.1 Image representation -- 5.2 Images as 2D signals -- 5.2.1 Linear space-invariant systems -- Properties of 2D convolution -- 5.2.2 Linear Circular Invariance systems -- 5.3 Frequency representation of 2D signals -- 5.3.1 Fourier transform of continuous signals -- 5.3.2 Discrete-space Fourier transform -- 5.3.3 2D discrete Fourier transform -- 5.3.4 Discrete cosine transform -- 5.4 Image sampling -- 5.4.1 Introduction -- 5.4.2 Basics on 2D sampling theory -- 5.4.2.1 Inexact reconstruction -- 5.4.3 Nyquist sampling density -- 5.5 Image interpolation -- 5.5.1 Typical interpolator kernels -- 5.5.1.1 Windowed sinc -- 5.5.1.2 Nearest neighbor interpolation -- 5.5.1.3 Linear interpolation -- 5.5.1.4 Cubic interpolation -- 5.6 Image quantization -- 5.7 Further reading -- 5.8 Exercises -- References -- 6 Image filtering: enhancement and restoration -- 6.1 Medical imaging filtering -- 6.2 Point-to-point operations -- 6.2.1 Basic operations -- 6.2.2 Contrast enhancement -- 6.2.3 Histogram processing -- Histogram equalization -- Histogram specification -- 6.3 Spatial operations -- 6.3.1 Linear filtering -- Smoothing filters -- Highlighting borders and small details -- 6.3.2 Non-linear filters -- Median filter -- Pseudomedian filter -- 6.4 Operations in the transform domain -- 6.4.1 Linear filters in the frequency domain -- 6.4.2 Homomorphic processing -- 6.5 Model-based filtering: image restoration -- 6.5.1 Noise models -- 6.5.2 Point spread function -- 6.5.3 Image restoration methods -- 6.6 Further reading -- 6.7 Exercises -- References -- 7 Multiscale and multiresolution analysis -- 7.1 Introduction -- 7.2 The image pyramid -- 7.3 The Gaussian scale-space -- 7.4 Properties of the Gaussian scale-space -- 7.4.1 The frequency perspective -- 7.4.2 The semi-group property -- 7.4.3 The analytical perspective. , 7.4.4 The heat diffusion perspective -- 7.5 Scale selection -- 7.5.1 Blob detection -- 7.5.2 Edge detection -- 7.6 The scale-space histogram -- 7.7 Exercises -- References -- Part III Medical image segmentation -- 8 Statistical shape models -- 8.1 Introduction -- 8.2 Representing structures with points -- 8.3 Comparing shapes -- 8.4 Aligning two shapes -- 8.5 Aligning a set of shapes -- 8.5.1 Algorithm for aligning sets of shapes -- 8.5.2 Example of aligning shapes -- 8.6 Building shape models -- 8.6.1 Choosing the number of modes -- 8.6.2 Examples of shape models -- 8.6.3 Matching a model to known points -- 8.7 Statistical models of texture -- 8.8 Combined models of appearance (shape and texture) -- 8.9 Image search -- 8.10 Exhaustive search -- 8.10.1 Regression voting -- 8.11 Alternating approaches -- 8.11.1 Searching for each point -- 8.11.2 Shape model as regularizer -- 8.12 Constrained local models -- 8.12.1 Iteratively updating parameters -- 8.12.2 Extracting features -- 8.12.3 Updating parameters -- 8.13 3D models -- 8.14 Recapitulation -- 8.15 Exercises -- References -- 9 Segmentation by deformable models -- 9.1 Introduction -- 9.2 Boundary evolution -- 9.2.1 Marker evolution -- 9.2.2 Level set evolution -- 9.3 Forces and speed functions -- 9.3.1 Parametric model forces -- 9.3.2 Geometric model speed functions -- 9.3.3 Non-conservative external forces -- 9.4 Numerical implementation -- 9.4.1 Parametric model implementation -- 9.4.2 Geometric model implementation -- 9.5 Other considerations -- 9.6 Recapitulation -- 9.7 Exercises -- References -- 10 Graph cut-based segmentation -- 10.1 Introduction -- 10.2 Graph theory -- 10.2.1 What is a graph? -- 10.2.2 Flow networks, max flow, and min cut -- 10.3 Modeling image segmentation using Markov random fields -- 10.4 Energy function, image term, and regularization term -- 10.4.1 Image term. , 10.4.2 Regularization term -- 10.5 Graph optimization and necessary conditions -- 10.5.1 Energy minimization and minimum cuts -- 10.5.2 Necessary conditions -- 10.5.3 Minimum cut graph construction -- 10.5.4 Limitations (and solutions) -- 10.6 Interactive segmentation -- 10.6.1 Hard constraints and user interaction -- 10.6.2 Example: coronary arteries in CT angiography -- 10.7 More than two labels -- 10.7.1 Move-making algorithm(s) -- 10.7.2 Ordered labels and convex priors -- 10.7.3 Optimal surfaces -- 10.7.4 Example: airways in CT -- 10.8 Recapitulation -- 10.9 Exercises -- References -- Part IV Medical image registration -- 11 Points and surface registration -- 11.1 Introduction -- 11.2 Points registration -- 11.2.1 Procrustes analysis for aligning corresponding point sets -- 11.2.2 Quaternion algorithm for registering two corresponding point sets -- 11.2.3 Iterative closest point algorithm for general points registration -- 11.2.4 Thin plate spline for non-rigid alignment of two corresponding point sets -- 11.3 Surface registration -- 11.3.1 Surface mesh representation -- 11.3.2 Surface parameterization -- 11.3.2.1 Conformal open surface parameterization -- 11.3.2.2 Area-preserving spherical parameterization -- 11.3.3 Surface registration strategies -- 11.3.3.1 SPHARM surface registration -- 11.3.3.2 Landmark-guided SPHARM surface registration -- 11.3.3.3 Landmark-guided open surface registration -- 11.4 Summary -- 11.5 Exercises -- References -- 12 Graph matching and registration -- 12.1 Introduction -- 12.2 Graph-based image registration -- 12.2.1 Graphical model construction -- 12.2.2 Optimization -- 12.2.3 Application to lung registration -- 12.2.4 Conclusion -- 12.3 Exercises -- References -- 13 Parametric volumetric registration -- 13.1 Introduction to volumetric registration -- 13.1.1 Definition and applications-- 13.1.2 VR as energy minimization.

Additional Edition: Print version: Frangi, Alejandro Medical Image Analysis San Diego : Elsevier Science & Technology,c2023 ISBN 9780128136577

Language: English