本書的主要內(nèi)容包括：光學相干斷層成像（OCT）技術(shù)及其在視網(wǎng)膜上的臨床應(yīng)用；視網(wǎng)膜OCT圖像分析預(yù)處理技術(shù)的主要步驟及算法；OCT圖像中視網(wǎng)膜解剖結(jié)構(gòu)的自動檢測和分析技術(shù)；OCT圖像中視網(wǎng)膜病變的自動檢測和分析技術(shù)；多模態(tài)視網(wǎng)膜圖像分析技術(shù)；以及OCT成像及分析技術(shù)的最新進展和展望。
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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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