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E-raamat: Landmark-Based Image Analysis: Using Geometric and Intensity Models

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Landmarks are preferred image features for a variety of computer vision tasks such as image mensuration, registration, camera calibration, motion analysis, 3D scene reconstruction, and object recognition. Main advantages of using landmarks are robustness w. r. t. lightning conditions and other radiometric vari­ ations as well as the ability to cope with large displacements in registration or motion analysis tasks. Also, landmark-based approaches are in general com­ putationally efficient, particularly when using point landmarks. Note, that the term landmark comprises both artificial and natural landmarks. Examples are comers or other characteristic points in video images, ground control points in aerial images, anatomical landmarks in medical images, prominent facial points used for biometric verification, markers at human joints used for motion capture in virtual reality applications, or in- and outdoor landmarks used for autonomous navigation of robots. This book covers the extraction oflandmarks from images as well as the use of these features for elastic image registration. Our emphasis is onmodel-based approaches, i. e. on the use of explicitly represented knowledge in image analy­ sis. We principally distinguish between geometric models describing the shape of objects (typically their contours) and intensity models, which directly repre­ sent the image intensities, i. e. ,the appearance of objects. Based on these classes of models we develop algorithms and methods for analyzing multimodality im­ ages such as traditional 20 video images or 3D medical tomographic images.
Preface xi
Introduction and Overview
1(34)
Image Analysis as a Knowledge-Based Process
4(3)
Model-based and Knowledge-Based Approaches
7(6)
Image Analysis and Image Synthesis
13(3)
Geometric Models and Intensity Models
16(3)
Differential Models and Deformable Models
19(2)
Parametric and Nonparametric Models
21(5)
Feature-and Landmark-Based Image Analysis
26(2)
Overview of Our Work
28(7)
General Characterization
29(2)
Feature Extraction
31(1)
Performance Characterization
32(1)
Elastic Image Registration
33(2)
Detection and Localization of Point Landmarks
35(74)
Motivation and General Approach
36(5)
3D Anatomical Point Landmarks
36(3)
A Framework for Developing Landmark Operators
39(1)
Semi-Automatic Landmark Extraction
40(1)
Definition and Characterization of Anatomical Point Landmarks
41(6)
Problems
42(1)
2D Landmarks
43(2)
3D Landmarks
45(2)
Mathematical Description of Anatomical Point Landmarks
47(5)
General Mathematical Descriptions
47(3)
Applicable Approaches to Landmark Extraction
50(2)
3D Differential Operators Based on Curvature Properties of Isocontours
52(6)
3D Generalization of Differential Corner Operators
58(29)
2D Operators
60(3)
3D Operators
63(5)
nD Operators
68(2)
Experimental Results
70(8)
Statistical Interpretation
78(3)
Interpretation as Principal Invariants
81(3)
Multi-step Approaches for Subvoxel Localization
84(3)
Parametric Approaches
87(19)
Radiometric Model and Imaging Process
90(3)
Parametric Intensity Models of Landmarks
93(7)
Model Fitting
100(2)
Experimental Results
102(4)
Summary and Conclusion
106(3)
Performance Characterization of Landmark Operators
109(70)
General Approach
110(2)
Analytic Studies
112(39)
Localization Accuracy
112(5)
Localization Precision in 2D Images
117(26)
Localization Precision in 3D Images
143(8)
Experimental Studies
151(25)
Operator Responses
151(1)
Statistical Measures for the Detection Performance
152(4)
Detection Performance Visualization and Measure Φ
156(12)
Number of Corresponding Points
168(1)
Localization Accuracy
169(2)
Affine Registration Accuracy
171(1)
Projective Invariants
172(4)
Summary and Conclusion
176(3)
Elastic Registration of Multimodality Images
179(80)
Background and Motivation
183(5)
General Registration Scheme
183(3)
Point-Based Elastic Registration
186(2)
Clinical Applications
188(3)
Interpolating Thin-Plate Splines
191(7)
Relation to Elasticity Theory
198(7)
Bending of a Thin Plate
198(3)
Relation to the Navier Equation
201(4)
Approximating Thin-Plate Splines with Isotropic Errors
205(11)
Theory
206(5)
Experimental Results
211(5)
Approximating Thin-Plate Splines with Anisotropic Errors
216(25)
Incorporation of Weight Matrices
217(4)
Special Cases of 2D and 3D Images
221(3)
Combination of Weight Matrices
224(2)
Estimation of Landmark Localization Uncertainties
226(6)
Experimental Results
232(9)
Orientation Attributes at Landmarks
241(6)
Biomechanical Modelling of Brain Deformations
247(3)
Related Work
250(5)
Conclusion and Future Work
255(4)
Bibliography 259(42)
Index 301