Contents Preface Chapter 1 Clinical Applications of Retinal Optical Coherence 1.1 Anatomy of the Eye and Retina 1 1.1.1 Simple Anatomy of the Eye 1 1.1.2 Simple Histology of Retina 2 1.1.3 Normal Macular OCT Image 4 1.2 Vitreomacular Interface Diseases 5 1.2.1 Vitreomacular Adhesion 5 1.2.2 Vitreomacular Traction 6 1.2.3 Full Thickness Macular Hole (FTMH) 7 1.2.4 Epiretinal Membrane 8 1.2.5 Myopic Traction Maculopathy 10 1.3 Glaucoma and Optic Neuropathy 10 1.3.1 Parapapillary Retinal Nerve Fiber Layer Thickness 11 1.3.2 Macular Ganglion Cell Thickness 11 1.3.3 0ptic Nerve Head Morphology 12 1.4 Retinal Vascular Diseases 14 1.4.1 Retinal Artery Occlusion 14 1.4.2 Diabetic Retinopathy 15 1.4.3 Retinal Vein Occlusion 16 1.5 0uter Retinal Degenerative Diseases 19 1.6 Choroidal Neovascularization and Polypoidal Choroidal Chapter 2 Fundamentals of Retinal Optical Coherence Tomography 26 2.1 Introduction 26 2.2 Developments and Principles of Operation of Optical Coherence 2.2.1 Time Domain OCT 27 2.2.2 Fourier Domain OCT 28 2.2.3 0ther Evolving OCT Technologies 30 2.3 Interpretation of the Optical Coherence Tomography Image 32 Chapter 3 Speckle Noise Reduction and Enhancement for OCT Images 38 3.1.2 Speckle Properties 40 3.2 0CT Image Modeling 41 3.3 Statistical Model for OCT Contrast Enhancement 47 3.4 Data Adaptive Transform Models for OCT Denoising 50 3.4.1 Conventional Dictionary Learning 50 3.4.2 Dual Tree Complex Wavelet Transform 51 3.4.3 Dictionary Learning with Wise Selection of Start Dictionary 52 3.5 Non Data Adaptive Transform Models for OCT Denoising 56 3.5.1 Denoising by Minimum Mean Square Error (MMSE) Estimator .58 Chapter 4 Reconstruction of Retinal OCT Images with Sparse 4.1 Introduction 75 4.2 Sparse Representation for Image Reconstruction 77 4.3 Sparsity Based on Methods for the OCT Image Reconstruction 78 4.3.1 Multiscale Sparsity Based on Tomographic Denoising (MSBTD) 78 4.3.2 Sparsity Based on Simultaneous Denoising and Interpolation (SBSDI) 86 4.3.3 3D Adaptive Sparse Representation Based on Compression 4.4 Conclusions 102 References 104 Chapter 5 Segmentation of OCT Scans Using Probabilistic Graphical 5.1 Introduction 109 5.2 A Probabilistic Graphical Model for Retina Segmentation 111 5.2.1 The Graphical Model 111 5.2.2 Variationallnference 114 5.3 Results 117 Contents v 5.3.1 Segmentation Performance 117 5.3.2 Pathology Detection 121 5.4 Segmenting Pathological Scans 125 5.5.1 Conclusion 127 5.5.2 Prospective Work 127 A Appendix 128 A.l Derivation of the Objective (5.16) 128 A.2 0ptimization with Respect to qb 132 References 134 Chapter 6 Diagnostic Capability of Optical Coherence Tomography Based Quantitative Analysis for Various Eye Diseases and Additional Factors Affecting Morphological 6.1 Introduction 137 6.2 0CT Based Retinal Morphological Measurements .140 6.2.1 Quantitative Measurements of Retinal Morphology 140 6.2.2 Quality, Artifacts, and Errors in Optical Coherence Tomography 6.2.3 Effect of Axial Length on Thickness 144 6.3 Capability of Optical Coherence Tomography Based Quantitative Analysis for Various Eye Diseases 147 6.3.1 Diabetic Retinopathy 148 6.3.2 Multiple Sclerosis 150 6.3.3 Amblyopia 156 6.4 Concluding Remarks 163 References 165 Chapter 7 Quantitative Analysis of Retinal Layers' Opticallntensities Based on Optical Coherence Tomography 182 7.1 Introduction 182 7.2 Automatic Layer Segmentation in OCT Images 184 7.3 The Optical Intensity of Retinal Layers of Normal Subjects 185 7.3.1 Data Acquisition 185 7.3.2 Statistical Analysis 185 7.3.3 Results of Quantitative Analysis of Retinal Layer Optical Intensities of Normal Subjects 185 7.3.4 Discussion 188 7.4 Distribution and Determinants of the Opticallntensity of Retinal Layers of Normal Subjects 188 7.4.1 Data Acquisition and Image Processing 189 7.4.2 Statistical Analysis 190 7.4.3 Retinal Optical Intensity Measurement 190 7.4.4 Determinants of Retinal Optical Intensity 194 7.4.5 Discussion 195 7.5 The Opticallntensity Distribution in Central Retinal Artery 7.5.1 Central Retinal Artery Occlusion 195 7.5.2 Subjects and Data Acquisition 196 7.5.3 Image Analysis 197 7.5.5 Discussion 200 References 203 Chapter 8 Segmentation of Optic Disc and Cup to Disc Ratio Quantification Based on OCT Scans 207 8.1 Introduction 207 8.2 0ptic Disc Segmentation 209 8.2.1 0verview of the Method 210 8.2.2 Coarse Disc Margin Location 211 8.2.3 SVM Based Patch Searching 214 8.3 Evaluation of Optic Disc Segmentation and C/D Ratio Quantification 216 8.3.1 Evaluation of Optic Disc Segmentation 216 8.3.2 Evaluation of C/D Ratio Quantification 219 References 222 Chapter 9 Choroidal OCT Analytics 225 9.1 Introduction 225 9.2 Automated Segmentation and High level Analytics 226 9.2.1 Problem Setup and Solution Approaches 226 9.2.2 Materials and Methods 228 9.2.3 Results and Statistical Analysis 233 9.3 Fine Grain Analysis 247 9.3.1 Problem Setup and Solution Approaches 248 9.3.3 Stromal Lumial Analysis: Experimental Results 252 References 255 Chapter 10 Layer Segmentation and Analysis for Retina with Diseases 259 10.1 Intorduction 259 10.2 Segmentation of Retinal Layers with Serous Pigment Epithelial Detachments 260 10.2.3 Results 269 10.3 Quantification of External Limiting Membrane Disruption Caused by Diabetic Macular Edema 275 10.4 Detection of Photoreceptor Ellipsoid Zone Disruption Caused by Trauma 282 10.4.3 Results 287 10.5 Conclusions 292 References 292 Chapter 11 Segmentation and Visualization of Drusen and Geographic Atrophy in SD OCT Images 299 11.1 Introduction 299 11.1.2 Geographic Atrophy 301 11.2 Drusen Segmentation and Visualization 301 11.2.1 Automated Drusen Segmentation and Quantification in SD OCT 11.2.2 An Improved OCT Derived Fundus Projectionlmage for Drusen 11.3 Geographic Atrophy Segmentation and Visualization 323 11.3.1 Semi Automatic Geographic Atrophy Segmentation for SD OCT 11.3.2 Automated Geographic Atrophy Segmentation for SD OCT Images Using Region Based C V Model via Local Similarity Factor 330 11.3.3 Restricted Summed Area Projection for Geographic Atrophy Visualization in SD OCT Images 340 11.3.4 A False Color Fusion Strategy for Drusen and GA Visualization in OCTImages 348 11.4 Conclusion 360 References 360 Chapter 12 Segmentation of Symptomatic Exudate Associated Derangements in 3D OCT Images 367 12.1 Introduction 367 12.2 Related Methods 369 12.2.1 Conventional Graph Cut Algorithm 369 12.2.2 0ptimal Surface Approach Graph Search Approach 369 12.3 Probability Constrained Graph Search Graph Cut 369 12.3.1 Initialization 370 12.3.2 Graph Search Graph Cut SEAD Segmentation 373 12.4 Performance Evaluation 377 12.4.1 Experimental Methods 377 12.4.2 Assessment of Initialization Performance 378 12.4.3 Assessment of Segmentation Performance 379 12.4.4 Statistical Correlation Analysis and Reproducibility Analysis 3 80 12.5 Conclusion 381 12.5.1 Importance of SEAD Segmentation 381 12.5.2 Advantages of the Probability Constrained Graph Cut Graph Search 12.5.3 Limitations of the Reported Method 383 12.5.4 Segmentation of Abnormal Retinal Layers 384 References 384 Chapter 13 Modeling and Prediction of Choroidal Neovascularization Growth Based on Longitudinal OCT Scans 389 13.1 Introduction 389 13.2.1 Method Overview 391 13.2.2 Data Acquisition 392 13.2.3 Preprocessing 393 13.2.4 Meshing 394 13.2.5 CNV Growth Model 395 13.2.6 Estimation of Growth Parameters 395 13.3 Experimental Results 397 13.4 Conclusions 399 References 399
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