| Description |
1 online resource (248 pages) |
| Contents |
Intro -- Diabetes and Retinopathy -- Copyright -- Contents -- Contributors -- Chapter 1: Complementary capabilities of photoacoustic imaging to existing optical ocular imaging techniques -- References -- Chapter 2: Intraretinal fluid map generation in optical coherence tomography images -- 1. Introduction -- 2. Optical coherence tomography: Background and significance -- 3. The classical segmentation approach -- 4. Fluid identification by means of a regional analysis -- 4.1. ROI extraction -- 4.2. Image sampling -- 4.3. Classification -- 4.4. Binary map creation -- 4.5. Color map creation |
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5. Discussion and conclusions -- Acknowledgments -- References -- Chapter 3: Fully automated identification and clinical classification of macular edema using optical coherence tomography ... -- 1. Background and significance -- 2. Computational identification and characterization of the MEs -- 2.1. Region of interest delimitation -- 2.2. Identification of the different types of macular edema -- 3. Results and discussion -- 4. Conclusions -- Acknowledgments -- References -- Chapter 4: Optimal surface segmentation with subvoxel accuracy in spectral domain optical coherence tomography images |
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1. Introduction -- 2. Methods -- 2.1. Problem formulation and energy function -- 2.1.1. Original formulation in regularly sampled space -- 2.1.2. Formulation in irregularly sampled space to achieve subvoxel accuracy -- 2.2. Graph construction -- 2.2.1. Intracolumn edges -- 2.2.2. Intercolumn edges -- 2.2.3. Intersurface edges -- 2.3. Surface recovery from minimum s-t cut -- 3. Experimental methods -- 3.1. Data -- 3.2. Workflow -- 3.2.1. Experiment for subvoxel accuracy -- 3.2.2. Experiment for super-resolution accuracy -- 3.2.3. Cost function design -- 3.2.4. Gradient vector flow |
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3.2.5. Parameter setting -- 4. Results -- 4.1. Results for subvoxel accuracy -- 4.2. Results for super-resolution accuracy -- 5. Discussion and conclusions -- References -- Chapter 5: Analysis of optical coherence tomography images using deep convolutional neural network for maculopathy grading -- 1. Introduction -- 1.1. Macular edema -- 1.2. Age-related macular degeneration -- 1.3. Central serous chorioretinopathy -- 2. Retinal imaging modalities -- 2.1. Optical coherence tomography -- 3. Dataset description -- 4. TU-Net: A deep CNN architecture for maculopathy grading -- 4.1. Preprocessing |
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4.2. Proposed TU-Net architecture -- 5. Results and discussion -- 6. Conclusion -- References -- Chapter 6: Segmentation of retinal layers from OCT scans -- 1. Introduction -- 2. Method -- 2.1. Joint MGRF-based macula: Centred image segmentation -- 2.1.1. Shape model Psp(m) -- 2.1.2. Appearance model -- 2.2. 3D retinal layers segmentation -- 3. Experimental results -- 4. Conclusion -- References -- Chapter 7: Low-complexity computer-aided diagnosis for diabetic retinopathy -- 1. Introduction -- 2. Related work -- 3. Low-complexity CNN for diabetic retinopathy |
| Notes |
3.1. Mathematical model and architecture |
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Print version record |
| Subject |
Retina -- Diseases -- Diagnosis
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Diabetes -- Complications.
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Diabetic Retinopathy -- diagnosis
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Diabetes Complications
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Diabetes -- Complications
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Retina -- Diseases -- Diagnosis
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| Form |
Electronic book
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| Author |
Suri, Jasjit S.
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El-Baz, Ayman S.
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| ISBN |
0128174390 |
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9780128174395 |
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