- •Contents
- •1.1. Introduction to the Eye
- •1.2. The Anatomy of the Human Visual System
- •1.3. Neurons
- •1.4. Synapses
- •1.5. Vision — Sensory Transduction
- •1.6. Retinal Processing
- •1.7. Visual Processing in the Brain
- •1.8. Biological Vision and Computer Vision Algorithms
- •References
- •2.1. Introduction to Computational Methods for Feature Detection
- •2.2. Preprocessing Methods for Retinal Images
- •2.2.1. Illumination Effect Reduction
- •2.2.1.1. Non-linear brightness transform
- •2.2.2. Image Normalization and Enhancement
- •2.2.2.1. Color channel transformations
- •2.2.2.3. Local adaptive contrast enhancement
- •2.2.2.4. Histogram transformations
- •2.3. Segmentation Methods for Retinal Anatomy Detection and Localization
- •2.3.1. A Boundary Detection Methods
- •2.3.1.1. First-order difference operators
- •2.3.1.2. Second-order boundary detection
- •2.3.1.3. Canny edge detection
- •2.3.2. Edge Linkage Methods for Boundary Detection
- •2.3.2.1. Local neighborhood gradient thresholding
- •2.3.2.2. Morphological operations for edge link enhancement
- •2.3.2.3. Hough transform for edge linking
- •2.3.3. Thresholding for Image Segmentation
- •2.3.3.1. Segmentation with a single threshold
- •2.3.3.2. Multi-level thresholding
- •2.3.3.3. Windowed thresholding
- •2.3.4. Region-Based Methods for Image Segmentation
- •2.3.4.1. Region growing
- •2.3.4.2. Watershed segmentation
- •2.4.1. Statistical Features
- •2.4.1.1. Geometric descriptors
- •2.4.1.2. Texture features
- •2.4.1.3. Invariant moments
- •2.4.2. Data Transformations
- •2.4.2.1. Fourier descriptors
- •2.4.2.2. Principal component analysis (PCA)
- •2.4.3. Multiscale Features
- •2.4.3.1. Wavelet transform
- •2.4.3.2. Scale-space methods for feature extraction
- •2.5. Summary
- •References
- •3.1.1. EBM Process
- •3.1.2. Evidence-Based Medical Issues
- •3.1.3. Value-Based Evidence
- •3.2.1. Economic Evaluation
- •3.2.2. Decision Analysis Method
- •3.2.3. Advantages of Decision Analysis
- •3.2.4. Perspective in Decision Analysis
- •3.2.5. Decision Tree in Decision Analysis
- •3.3. Use of Information Technologies for Diagnosis in Ophthalmology
- •3.3.1. Data Mining in Ophthalmology
- •3.3.2. Graphical User Interface
- •3.4. Role of Computational System in Curing Disease of an Eye
- •3.4.1. Computational Decision Support System: Diabetic Retinopathy
- •3.4.1.1. Wavelet-based neural network23
- •3.4.1.2. Content-based image retrieval
- •3.4.2. Computational Decision Support System: Cataracts
- •3.4.2.2. K nearest neighbors
- •3.4.2.3. GUI of the system
- •3.4.3. Computational Decision Support System: Glaucoma
- •3.4.3.1. Using fuzzy logic
- •3.4.4. Computational Decision Support System: Blepharitis, Rosacea, Sjögren, and Dry Eyes
- •3.4.4.1. Utility of bleb imaging with anterior segment OCT in clinical decision making
- •3.4.4.2. Computational decision support system: RD
- •3.4.4.3. Role of computational system
- •3.4.5. Computational Decision Support System: Amblyopia
- •3.4.5.1. Role of computational decision support system in amblyopia
- •3.5. Conclusion
- •References
- •4.1. Introduction to Oxygen in the Retina
- •4.1.1. Microelectrode Methods
- •4.1.2. Phosphorescence Dye Method
- •4.1.3. Spectrographic Method
- •4.1.6. HSI Method
- •4.2. Experiment One
- •4.2.1. Methods and Materials
- •4.2.1.1. Animals
- •4.2.1.2. Systemic oxygen saturation
- •4.2.1.3. Intraocular pressure
- •4.2.1.4. Fundus camera
- •4.2.1.5. Hyperspectral imaging
- •4.2.1.6. Extraction of spectral curves
- •4.2.1.7. Mapping relative oxygen saturation
- •4.2.1.8. Relative saturation indices (RSIs)
- •4.2.2. Results
- •4.2.2.1. Spectral signatures
- •4.2.2.2. Oxygen breathing
- •4.2.2.3. Intraocular pressure
- •4.2.2.4. Responses to oxygen breathing
- •4.2.2.5. Responses to high IOP
- •4.2.3. Discussion
- •4.2.3.1. Pure oxygen breathing experiment
- •4.2.3.2. IOP perturbation experiment
- •4.2.3.3. Hyperspectral imaging
- •4.3. Experiment Two
- •4.3.1. Methods and Materials
- •4.3.1.1. Animals, anesthesia, blood pressure, and IOP perturbation
- •4.3.1.3. Spectral determinant of percentage oxygen saturation
- •4.3.1.5. Preparation and calibration of red blood cell suspensions
- •4.3.2. Results
- •4.3.2.2. Oxygen saturation of the ONH
- •4.3.3. Discussion
- •4.3.4. Conclusions
- •4.4. Experiment Three
- •4.4.1. Methods and Materials
- •4.4.1.1. Compliance testing
- •4.4.1.2. Hyperspectral imaging
- •4.4.1.3. Selection of ONH structures
- •4.4.1.4. Statistical methods
- •4.4.2. Results
- •4.4.2.1. Compliance testing
- •4.4.2.2. Blood spectra from ONH structures
- •4.4.2.3. Oxygen saturation of ONH structures
- •4.4.2.4. Oxygen saturation maps
- •4.4.3. Discussion
- •4.5. Experiment Four
- •4.5.1. Methods and Materials
- •4.5.2. Results
- •4.5.3. Discussion
- •4.6. Experiment Five
- •4.6.1. Methods and Materials
- •4.6.1.3. Automatic control point detection
- •4.6.1.4. Fused image optimization
- •4.7. Conclusion
- •References
- •5.1. Introduction to Thermography
- •5.2. Data Acquisition
- •5.3. Methods
- •5.3.1. Snake and GVF
- •5.3.2. Target Tracing Function and Genetic Algorithm
- •5.3.3. Locating Cornea
- •5.4. Results
- •5.5. Discussion
- •5.6. Conclusion
- •References
- •6.1. Introduction to Glaucoma
- •6.1.1. Glaucoma Types
- •6.1.1.1. Primary open-angle glaucoma
- •6.1.1.2. Angle-closure glaucoma
- •6.1.2. Diagnosis of Glaucoma
- •6.2. Materials and Methods
- •6.2.1. c/d Ratio
- •6.2.2. Measuring the Area of Blood Vessels
- •6.2.3. Measuring the ISNT Ratio
- •6.3. Results
- •6.4. Discussion
- •6.5. Conclusion
- •References
- •7.1. Introduction to Temperature Distribution
- •7.3. Mathematical Model
- •7.3.1. The Human Eye
- •7.3.2. The Eye Tumor
- •7.3.3. Governing Equations
- •7.3.4. Boundary Conditions
- •7.4. Material Properties
- •7.5. Numerical Scheme
- •7.5.1. Integro-Differential Equations
- •7.6. Results
- •7.6.1. Numerical Model
- •7.6.2. Case 1
- •7.6.3. Case 2
- •7.6.4. Discussion
- •7.7. Parametric Optimization
- •7.7.1. Analysis of Variance
- •7.7.2. Taguchi Method
- •7.7.3. Discussion
- •7.8. Concluding Remarks
- •References
- •8.1. Introduction to IR Thermography
- •8.2. Infrared Thermography and the Measured OST
- •8.3. The Acquisition of OST
- •8.3.1. Manual Measures
- •8.3.2. Semi-Automated and Fully Automated
- •8.4. Applications to Ocular Studies
- •8.4.1. On Ocular Physiologies
- •8.4.2. On Ocular Diseases and Surgery
- •8.5. Discussion
- •References
- •9.1. Introduction
- •9.1.1. Preprocessing
- •9.1.1.1. Shade correction
- •9.1.1.2. Hough transform
- •9.1.1.3. Top-hat transform
- •9.1.2. Image Segmentation
- •9.1.2.1. The region approach
- •9.1.2.2. The gradient-based method
- •9.1.2.3. Edge detection
- •9.1.2.3.2. The second-order derivative methods
- •9.1.2.3.3. The optimal edge detector
- •9.2. Image Registration
- •9.4. Automated, Integrated Image Analysis Systems
- •9.5. Conclusion
- •References
- •10.1. Introduction to Diabetic Retinopathy
- •10.2. Data Acquisition
- •10.3. Feature Extraction
- •10.3.1. Blood Vessel Detection
- •10.3.2. Exudates Detection
- •10.3.3. Hemorrhages Detection
- •10.3.4. Contrast
- •10.4.1. Backpropagation Algorithm
- •10.5. Results
- •10.6. Discussion
- •10.7. Conclusion
- •References
- •11.1. Related Studies
- •11.2.1. Encryption
- •11.3. Compression Technique
- •11.3.1. Huffman Coding
- •11.4. Error Control Coding
- •11.4.1. Hamming Codes
- •11.4.2. BCH Codes
- •11.4.3. Convolutional Codes
- •11.4.4. RS Codes14
- •11.4.5. Turbo Codes14
- •11.5. Results
- •11.5.1. Using Turbo Codes for Transmission of Retinal Fundus Image
- •11.6. Discussion
- •11.7. Conclusion
- •References
- •12.1. Introduction to Laser-Thermokeratoplasty (LTKP)
- •12.2. Characteristics of LTKP
- •12.3. Pulsed Laser
- •12.4. Continuous-Wave Laser
- •12.5. Mathematical Model
- •12.5.1. Model Description
- •12.5.2. Governing Equations
- •12.5.3. Initial-Boundary Conditions
- •12.6. Numerical Scheme
- •12.6.1. Integro-Differential Equation
- •12.7. Results
- •12.7.1. Pulsed Laser
- •12.7.2. Continuous-Wave Laser
- •12.7.3. Thermal Damage Assessment
- •12.8. Discussion
- •12.9. Concluding Remarks
- •References
- •13.1. Introduction to Optical Eye Modeling
- •13.1.1. Ocular Measurements for Optical Eye Modeling
- •13.1.1.1. Curvature, dimension, thickness, or distance parameters of ocular elements
- •13.1.1.2. Three-dimensional (3D) corneal topography
- •13.1.1.3. Crystalline lens parameters
- •13.1.1.4. Refractive index
- •13.1.1.5. Wavefront aberration
- •13.1.2. Eye Modeling Using Contemporary Optical Design Software
- •13.1.3. Optical Optimization and Merit Function
- •13.2. Personalized and Population-Based Eye Modeling
- •13.2.1. Customized Eye Modeling
- •13.2.1.1. Optimization to the refractive error
- •13.2.1.2. Optimization to the wavefront measurement
- •13.2.1.3. Tolerance analysis
- •13.2.2. Population-Based Eye Modeling
- •13.2.2.1. Accommodative eye modeling
- •13.2.2.2. Ametropic eye modeling
- •13.2.2.3. Modeling with consideration of ocular growth and aging
- •13.2.2.4. Modeling for disease development
- •13.2.3. Validation of Eye Models
- •13.2.3.1. Point spread function and modulation transfer function
- •13.2.3.2. Letter chart simulation
- •13.2.3.3. Night/day vision simulation
- •13.3. Other Modeling Considerations
- •13.3.1. Stiles Crawford Effect (SCE)
- •13.3.1.2. Other retinal properties
- •13.3.1.4. Optical opacity
- •13.4. Examples of Ophthalmic Simulations
- •13.4.1. Simulation of Retinoscopy Measurements with Eye Models
- •13.4.2. Simulation of PR
- •13.5. Conclusion
- •References
- •14.1. Network Infrastructure
- •14.1.1. System Requirements
- •14.1.2. Network Architecture Design
- •14.1.4. GUI Design
- •14.1.5. Performance Evaluation of the Network
- •14.2. Image Analysis
- •14.2.1. Vascular Tree Segmentation
- •14.2.2. Quality Assessment
- •14.2.3. ON Detection
- •14.2.4. Macula Localization
- •14.2.5. Lesion Segmentation
- •14.2.7. Patient Demographics and Statistical Outcomes
- •14.2.8. Disease State Assessment
- •14.2.9. Image QA
- •Acknowledgments
- •References
- •Index
Optical Eye Modeling and Applications
analysis to determine how critical the optimized values of the variables are for the prediction of the total wave aberration of the eye. Navarro has developed the following procedure for this particular optical design problem: once the optimization algorithm finds the minimum of the merit function, 1D plots of this merit function are obtained in the vicinity of the optimal or minimal value versus each independent variable (such as surface curvature, conic constant, decentrations, and tip/tilt angle in Navarro’s study). Here, we perform a similar procedure. Once the optimization algorithm finds the minimum of the merit function, we obtain plots of this merit function against the varied RMS for each order of Zernike coefficients of anterior lens around the optimal (minimum) value. From these plots, we can see which order is the most significant and dominant (the smallest tolerance).
13.2.2. Population-Based Eye Modeling
13.2.2.1. Accommodative eye modeling
Most schematic eyes represent emmetropic and relaxed adult eyes. Under accommodation demand for near vision, the ciliary muscles holding the crystalline lens tighten, thereby causing the lens to become more rounded. The thickness and curvatures on both surfaces of lens increase. In the history of eye modeling, a few accommodative models exist. The Gullstrand No. 1 model1 is constructed at about 10.9 diopter accommodation. The Gullstrand-Emsley9 and Le Grand models10 present full schematic eyes that are in accommodated forms of 8.6 and 7.1 diopters, respectively. The Navarro model eye is “adaptive” in the sense that it offers the variability of lens parameters (the thickness, the radius of curvatures, and conic constants on both surfaces) and the anterior chamber and vitreous depth. In this popular eye model, these ocular parameters are given as functions of accommodation level. Atchinson summarized the four accommodative eye models in Appendix A3.11
As shown in previous studies,12−15 the lens biometry is fundamentally independent of eye refractive error. However, in addition to the variation under accommodation, lens shape and refractive index are also significantly related to age. With an increase in age during adulthood, the lens becomes thicker and more curved in its relaxed state, and its refractive
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index distribution changes. Therefore, modeling the accommodative eye should be performed with specification of a limited age range.
13.2.2.2. Ametropic eye modeling
In 2006, Atchison published the optical models for myopic eyes11 according to the analysis of statistical relevance obtained from the subjects and studies primarily of his research group. Table 13.2 compares the refraction dependence of the Atchison myopic eye model and the emmetropic Navarro eye model.2 Not included in the table is the information regarding the decenters of the pupil and the lens, the tilt of the lens, and the fovea location that are also used in the models. These parameters contribute to ocular
Table 13.2. Comparison of parameters between Atchison myopia model11 and Navarro emmetropic model.2
|
|
Emmetropic |
|
|
|
condition |
Refractive error (K) |
Ocular parameter |
Model |
(K = 0) |
dependence |
Anterior corneal radius |
Atchison |
7.77 mm |
+0.022 mm/diopter |
of curvature |
Navarro |
7.72 mm |
|
Asphericity of anterior |
Atchison |
−0.15 |
Not significant |
cornea |
Navarro |
−0.26 |
|
|
|
||
Central corneal |
Atchison |
0.55 mm |
Not significant |
thickness |
Navarro |
0.55 mm |
|
Index of refraction of |
Atchison |
1.3975, 1.3807, 1.37405, 1.3668, for |
|
cornea |
Navarro |
λ = 365, 486.1, 656.3, 1014 nm |
|
|
|||
Posterior corneal radius |
Atchison |
6.40 mm |
Not significant |
|
Navarro |
6.50 mm |
|
Asphericity of posterior |
Atchison |
−0.275 |
Not significant |
cornea |
Navarro |
0 |
|
Anterior chamber depth |
Atchison |
3.15 mm |
Not significant |
|
Navarro |
3.05 mm |
|
Index of refraction of |
Atchison |
1.3593, 1.3422, 1.3354, 1.3278, for |
|
aqueous humor |
Navarro |
λ = 365, 486.1, 656.3, 1014 nm |
|
|
|||
(Continued)
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Optical Eye Modeling and Applications
Table 13.2. (Continued)
|
|
Emmetropic |
|
|
|
condition |
Refractive error (K) |
Ocular parameter |
Model |
(K = 0) |
dependence |
Anterior lens |
Atchison |
11.48 mm |
Not significant |
radius |
|
|
|
|
Navarro |
10.20 mm |
|
Anterior lens |
Atchison |
−5 |
Not significant |
asphericity |
Navarro |
−3.1316 |
|
|
|
||
Lens thickness |
Atchison |
3.6 mm |
Not significant |
|
Navarro |
4.0 mm |
|
Refractive index of |
Atchison |
Gradient index |
|
crystalline lens |
Navarro |
1.4492, 1.4263, 1.4175, 1.4097, for |
|
|
|
λ = 365, 486.1, 656.3, 1014 nm |
|
Posterior lens |
Atchison |
−5.9 mm |
Not significant |
radius |
|
|
|
Posterior lens asphericity
Vitreous chamber depth
Refractive index of vitreous humor
Radius of retina curvature
Navarro |
−6.0 mm |
|
Atchison |
−2 |
Omit; not significant |
Navarro |
−1 |
−0.299 mm/diopter |
Atchison |
16.28 mm |
|
Navarro |
16.32 mm |
|
Atchison |
1.3565, 1.3407, 1.3341, 1.3273, for |
|
Navarro |
α = 365, 486.1, 656.3, 1014 nm. |
|
Atchison |
−12.91 mm (x); |
−0.094 mm/diopter (x); |
|
−12.72 mm(y) |
+0.004 mm/diopter (y) |
Navarro |
−12 mm |
|
asymmetry and introduce astigmatism, coma, and irregular aberrations, and, therefore, have important effect upon optical performance. The significance of using these parameters depends on the types of applications.
13.2.2.3. Modeling with consideration of ocular growth and aging
Age is an important factor in ocular biometry. The ocular dimension increases significantly during the first year of life. During infancy, the ocular refraction develops from mild hyperopia to emmetropia. From the age
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