Diabetes and fundus OCT /

Bibliographic Details
Call Number:Libro Electrónico
Other Authors: El-Baz, Ayman S., Suri, Jasjit S.
Format: Electronic eBook
Language:Inglés
Published: Amsterdam : Elsevier, 2020.
Series:Computer-assisted diagnosis ; v. 1
Subjects:
Diabetic retinopathy > Diagnosis.
Retina > Tomography.
R�etine > Tomographie.
Online Access:Texto completo
Table of Contents:
  • Intro
  • Diabetes and Fundus OCT
  • Copyright
  • Contents
  • Contributors
  • Chapter 1: Computer-aided diagnosis system based on a comprehensive local features analysis for early diabetic retinopath ...
  • 1. Introduction
  • 2. Materials and methods
  • 2.1. Contrast enhancement and noise elimination
  • 2.2. Vessel segmentation
  • 2.3. Local feature extraction and diagnosis
  • 2.3.1. Blood vessel density estimation
  • 2.3.2. Retinal blood vessel caliber
  • 2.3.3. Width of the FAZ
  • 2.3.4. Bifurcation points
  • 2.4. Mild DR diagnosis
  • 3. Experimental results
  • 4. Conclusions
  • References
  • Chapter 2: Deep learning approach for classification of eye diseases based on color fundus images
  • 1. Introduction
  • 2. Deep learning
  • 2.1. Convolutional neural network
  • 3. Fundus imaging
  • 3.1. Image acquisition
  • 3.2. Main vascular abnormalities
  • 3.2.1. Tortuosity
  • 3.2.2. Generalized arteriolar narrowing
  • 3.2.3. Focal arteriolar narrowing
  • 3.2.4. Bifurcations abnormalities
  • 3.2.5. Crossing abnormalities
  • 3.3. Main nonvascular
  • 3.3.1. Microaneurysms and red dots
  • 3.3.2. Hemorrhages
  • 3.3.3. Hard exudates
  • 3.3.4. Cotton wool spots
  • 3.4. Hypertensive retinopathy
  • 3.5. Diabetic retinopathy
  • 4. Research method
  • 4.1. Research framework
  • 4.2. Dataset
  • 4.3. Preprocessing
  • 4.4. Classifier configuration
  • 4.5. Model training and cross validation
  • 4.6. Experiment result
  • 4.7. Conclusion
  • References
  • Further reading
  • Chapter 3: Fundus retinal image analyses for screening and diagnosing diabetic retinopathy, macular edema, and glaucoma d ...
  • 1. Introduction
  • 2. A brief history of fundus retinal imaging
  • 3. Public retinal image databases
  • 4. Automatic retinal image analysis
  • 4.1. Performance metrics used in ARIA
  • 4.2. Retinal vessel segmentation
  • 4.3. Optic disc (or ONH), macula and fovea: Detection and segmentation
  • 5. Macular edema and DR classification using fundus image
  • 5.1. Detection of microaneurysms and hemorrhage
  • 5.2. Detection of exudates
  • 5.3. Automatic classification of DR and DME severity levels
  • 6. Glaucoma classification using ARIA
  • 7. Future trends in fundus retinal imaging
  • 8. Conclusion
  • References
  • Chapter 4: Mobile phone-based diabetic retinopathy detection system
  • 1. Introduction
  • 1.1. Causes of diabetic retinopathy
  • 1.2. Types of diabetic retinopathy
  • 2. Related works
  • 3. Proposed system
  • 3.1. Artificial neural networks
  • 3.2. Discrete wavelet transform
  • 3.3. Smartphone as an ophthalmoscope
  • 3.4. Software requirement and description
  • 3.5. Algorithm and flow chart
  • 4. Experimental results
  • 5. Conclusion
  • References
  • Chapter 5: Computer-aided diagnosis of age-related macular degeneration by OCT, fundus image analysis
  • 1. Introduction
  • 1.1. Risk factors
  • 1.2. Symptoms of macular degeneration
  • 1.2.1. Macular degeneration diagnosis, treatment, and prevention
  • 1.3. Diagnosis
  • 1.4. AMD treatment

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