Preface
Chapter 1 Clinical Applications of Retinal Optical Coherence
1.1 Anatomy of the Eye and Retina
1.1.1 Simple Anatomy of the Eye
1.1.2 Simple Histology of Retina
1.1.3 Normal Macular OCT Image
1.2 Vitreomacular Interface Diseases
1.2.1 Vitreomacular Adhesion
1.2.2 Vitreomacular Traction
1.2.3 Full Thickness Macular Hole (FTMH)
1.2.4 Epiretinal Membrane
1.2.5 Myopic Traction Maculopathy
1.3 Glaucoma and Optic Neuropathy
1.3.1 Parapapillary Retinal Nerve Fiber Layer Thickness
1.3.2 Macular Ganglion Cell Thickness
1.3.3 0ptic Nerve Head Morphology
1.4 Retinal Vascular Diseases
1.4.1 Retinal Artery Occlusion
1.4.2 Diabetic Retinopathy
1.4.3 Retinal Vein Occlusion
1.5 0uter Retinal Degenerative Diseases
1.6 Choroidal Neovascularization and Polypoidal Choroidal
Chapter 2 Fundamentals of Retinal Optical Coherence Tomography
2.1 Introduction
2.2 Developments and Principles of Operation of Optical Coherence
2.2.1 Time Domain OCT
2.2.2 Fourier Domain OCT
2.2.3 0ther Evolving OCT Technologies
2.3 Interpretation of the Optical Coherence Tomography Image
Chapter 3 Speckle Noise Reduction and Enhancement for OCT
Images
3.1.2 Speckle Properties
3.2 0CT Image Modeling
3.3 Statistical Model for OCT Contrast Enhancement
3.4 Data Adaptive Transform Models for OCT Denoising
3.4.1 Conventional Dictionary Learning
3.4.2 Dual Tree Complex Wavelet Transform
3.4.3 Dictionary Learning with Wise Selection of Start Dictionary
3.5 Non Data Adaptive Transform Models for OCT Denoising
3.5.1 Denoising by Minimum Mean Square Error (MMSE) Estimator
Chapter 4 Reconstruction of Retinal OCT Images with Sparse
4.1 Introduction
4.2 Sparse Representation for Image Reconstruction
4.3 Sparsity Based on Methods for the OCT Image Reconstruction
4.3.1 Multiscale Sparsity Based on Tomographic Denoising (MSBTD)
4.3.2 Sparsity Based on Simultaneous Denoising and Interpolation
(SBSDI)
4.3.3 3D Adaptive Sparse Representation Based on Compression
4.4 Conclusions
References
Chapter 5 Segmentation of OCT Scans Using Probabilistic Graphical
5.1 Introduction
5.2 A Probabilistic Graphical Model for Retina Segmentation
5.2.1 The Graphical Model
5.2.2 Variationallnference
5.3 Results
5.3.1 Segmentation Performance
5.3.2 Pathology Detection
5.4 Segmenting Pathological Scans
5.5.1 Conclusion
5.5.2 Prospective Work
A Appendix
A.l Derivation of the Objective (5.16)
A.2 0ptimization with Respect to qb
References
Chapter 6 Diagnostic Capability of Optical Coherence Tomography Based
Quantitative Analysis for Various Eye Diseases and
Additional Factors Affecting Morphological
6.1 Introduction
6.2 0CT Based Retinal Morphological Measurements .
6.2.1 Quantitative Measurements of Retinal Morphology
6.2.2 Quality, Artifacts, and Errors in Optical Coherence Tomography
6.2.3 Effect of Axial Length on Thickness
6.3 Capability of Optical Coherence Tomography Based Quantitative
Analysis for Various Eye Diseases
6.3.1 Diabetic Retinopathy
6.3.2 Multiple Sclerosis
6.3.3 Amblyopia
6.4 Concluding Remarks
References
Chapter 7 Quantitative Analysis of Retinal Layers' Opticallntensities
Based on Optical Coherence Tomography
7.1 Introduction
7.2 Automatic Layer Segmentation in OCT Images
7.3 The Optical Intensity of Retinal Layers of Normal Subjects
7.3.1 Data Acquisition
7.3.2 Statistical Analysis
7.3.3 Results of Quantitative Analysis of Retinal Layer Optical Intensities of
Normal Subjects
7.3.4 Discussion
7.4 Distribution and Determinants of the Opticallntensity of Retinal Layers
of Normal Subjects
7.4.1 Data Acquisition and Image Processing
7.4.2 Statistical Analysis
7.4.

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