Diabetes and retinopathy. Volume 2, Computer-aided diagnosis /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Suri, Jasjit S., El-Baz, Ayman S.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam + : Elsevier, 2020.
Temas:
Retina > Diseases > Diagnosis.
Diabetes > Complications.
Diabetic Retinopathy > diagnosis
Diabetes Complications
Diab�ete > Complications et s�equelles.
Diabetes > Complications
Retina > Diseases > Diagnosis
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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

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