| Preface | 6 |
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| Contents | 8 |
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| Automatic Segmentation of the Optic Radiation Using DTI in Healthy Subjects and Patients with Glaucoma | 12 |
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| 1 Introduction | 13 |
| 2 Interpolation in the Space of Diffusion Tensors | 15 |
| 3 Initial Estimation of the Optic Radiation and the Midbrain | 16 |
| 4 Segmentation Using a Statistical Level Set Framework | 18 |
| 5 Results and Discussion | 20 |
| 6 Conclusion and Future Work | 23 |
| References | 24 |
| Real Time Colour Based Player Tracking in Indoor Sports | 27 |
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| 1 Introduction | 28 |
| 2 Related Work | 29 |
| 3 Architecture | 30 |
| 3.1 Projected Solution | 31 |
| 3.2 Tested Solution | 31 |
| 4 Image Processing | 32 |
| 4.1 Team Definition | 32 |
| 4.2 Background Subtraction | 33 |
| 4.3 Colour Detection | 34 |
| 4.4 Blob Aggregation and Characterization | 34 |
| 4.5 Real World Transformation | 36 |
| 4.6 Player Tracking | 37 |
| 5 Results | 38 |
| 5.1 Overview | 38 |
| 5.2 Sample Footage | 39 |
| 5.3 Player Detection | 39 |
| 5.4 Player Tracking | 41 |
| 6 Conclusions and Future Work | 44 |
| References | 45 |
| Visualization of the Dynamics of the Female Pelvic Floor Reflex and Steady State Function | 46 |
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| 1 Introduction | 47 |
| 1.1 Clinical Problem | 47 |
| 1.2 Anatomical Considerations | 47 |
| 1.3 Functional Considerations | 48 |
| 1.4 Contribution of Imaging | 48 |
| 1.5 Diagnostic Methods | 49 |
| 1.6 Evaluation of the Dynamic Function of the PF Using 2D Ultrasound Imaging | 50 |
| 2 Methods | 51 |
| 2.1 Coordinate System of the Anatomic Structures | 51 |
| 2.2 Motion Tracking Algorithms | 52 |
| 2.3 Image Segmentation Algorithms | 53 |
| 3 Results | 55 |
| 3.1 Quantitative Analysis of the Static Characters of the UVJ-ARA-SP Triangle | 57 |
| 3.2 Automatic Detection of the UVJ-ARA-SP Triangle | 59 |
| 3.3 Quantitative Analysis of the Dynamic Characters of the UVJ-ARA-SP Triangle | 60 |
| 3.4 Quantitative Measurement of Dynamic Parameters of the UVJ-ARA-SP Triangle | 61 |
| 3.5 The Kinematical Analysis of the Activities of the UVJ-ARA-SP Triangle | 63 |
| 3.6 Motion Tracking Algorithms | 65 |
| 3.7 Visualization of the Dynamic Profiles of the Urethra | 65 |
| 3.8 Visualization of the Timing of the Dynamic Profiles | 67 |
| 4 Bio Mechanical Properties of Pelvic Floor Function Using the Vaginal Probe | 71 |
| 4.1 Temporal/Spatial Visualization | 73 |
| 4.2 Resting Closure Profiles | 73 |
| 5 Discussion | 75 |
| References | 80 |
| Population Exposure and Impact Assessment: Benefits of Modeling Urban Land Use in Very High Spatial and Thematic Detail | 84 |
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| 1 Introduction | 84 |
| 2 Data and Study Area | 85 |
| 2.1 Study Area | 85 |
| 2.2 Remote Sensing Data and Ancillary Space-Related Information | 87 |
| 3 Multi-Source Modeling of Functional Urban Patterns | 87 |
| 3.1 Object Based Image Analysis and Integrated Land Cover Classification | 88 |
| 3.2 Progressing from Land Cover to Land Use Assessment by Adding Ancillary Space-Related Information | 88 |
| 4 Spatial Analysis of Population Distribution Patterns | 90 |
| 5 Exposure and Impact Assessment | 91 |
| 5.1 Population Exposure to Earthquake Hazard | 92 |
| 5.2 Street Noise Propagation and Affected Population | 94 |
| 6 Conclusion and Outlook | 95 |
| References | 97 |
| Dynamic Radiography Imaging as a Tool in the Design and Validation of a Novel Intelligent Amputee Socket | 99 |
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| 1 The Need for Novel Socket Designs in a Constantly Increasing Amputee Population | 99 |
| 2 Current State-of-the-Art Socket Evaluation Methodologies Are Inefficient in Assessing Trans-Tibial (TT) Socket Problems | 100 |
| 3 Integrating Dynamic Radiographic Imaging with Computer-Aided Design and Computational Modeling in Socket Evaluation | 103 |
| 4 SMARTsocket: An Example of Integration of Dynamic Imaging, CAD-CAE and FE Methods in Socket Evaluation | 105 |
| 5 Conclusion | 116 |
| References | 116 |
| A Discrete Level Set Approach for Texture Analysis of Microscopic Liver Images | 121 |
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| 1 Introduction | 121 |
| 2 Mathematical Formulation | 123 |
| 2.1 Discrete Level Set Theory | 123 |
| 2.2 Texture Analysis of Liver Tissue | 125 |
| 2.3 The Proposed Algorithm | 126 |
| 3 Morphological and Texture Parameters Identification | 126 |
| 4 Numerical Results | 127 |
| 5 Conclusions | 130 |
| References | 131 |
| Deformable and Functional Models | 132 |
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| 1 Introduction | 132 |
| 2 Deformable Models | 133 |
| 2.1 Energy-Minimizing Snakes | 133 |
| 2.2 Dynamic Snakes | 135 |
| 2.3 Discretization and Numerical Simulation | 136 |
| 2.4 Probabilistic (Bayesian) Interpretation | 138 |
| 2.5 Higher-Dimensional Generalizations | 139 |
| 2.5.1 Deformable Surfaces | 139 |
| 2.6 Topology-Adaptive Deformable Models | 140 |
| 2.6.1 Topology-Adaptive Snakes | 140 |
| 2.6.2 Topology-Adaptive Deformable Surfaces | 142 |
| 2.7 Deformable Organisms | 143 |
| 3 Functional Models | 145 |
| 3.1 Facial Simulation | 146 |
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