- •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
Ying-Ling Chen et al.
studies of tear film models.19−21 These studies show that the optical parameters of the tear film that can affect the accuracy and completeness of optical eye modeling include tear film thickness, post-blink tear undulation, tear breakup pattern, eyelid-produced bumps and ridges, bubbles, and rough pre-contact lens tear surfaces. These tear film characteristics in spatial and temporal domains can be included in the schematic eye models. The predictive modeling and simulation could yield insightful information regarding the dry eye vision and promote the diagnostics technology for the disease.
13.3.1.4. Optical opacity
The only published cataract eye model is proposed by Donnelly.22 In this study, Scheimpflug cameras characterized the anterior segment and backscatter from cataract. He discussed how to measure and model intraocular light scatter with SH wavefront-sensing data.22 The key to simulate the cataract based on our personalized eye models is to simulate the surface and volumetric scattered light in the eye, both of which contribute to contrast reduction of the image at the retina. The scattering theory to be applied is Rayleigh-Mie scattering, since cataracts are volumes composed of small scattering particles.22,23 The scattering of cataracts can be simulated with a bi-directional scatter distribution function (BSDF) in a nonsequential component in ZEMAX.
13.4. Examples of Ophthalmic Simulations
Eye models have applications in scientific research and industrial engineering. Patient vision simulation is useful for medical education and for patient consultation. Ophthalmic measurement simulations are advantageous for even more applications. As demonstrations, the simulations of retinoscopy and the photorefraction (PR) measurement are described in this section.
13.4.1.Simulation of Retinoscopy Measurements with Eye Models
Retinoscopy was introduced more than 100 years ago and is still practiced today to yield important clinical results. A traditional “spot retinoscope”
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projects a spotlight onto a patient’s eye at a distance of 0.5–1.0 meter, while a contemporary “streak retinoscope” projects a straight-filament image. The size of the spot or streak projection is adjustable by moving a condenser lens that is located above the light source (or filament) in the handle of the device. The retinal reflex is observed by the examiner through a peephole on the scope. When moving the streak projection across the patient’s pupil, the reflex of a myopic or hyperopic eye appears to move with or against the projection motion. The moving speed and direction of reflex depend on the position of the condenser lens. Subsequently, the examiner uses a phoropter or manually places trial lenses over the examinee’s eye to “neutralize” the reflex movement.
When the refraction is neutralized, the pupil will suddenly appear bright, as the light projection aligns to the center of pupil, and will turn dark if the projection slightly misaligns toward either side. No movement should be seen under the neutralization condition. The compensation lens indicates the required defocus correction. Retinoscopic measurement is objective and, therefore, especially useful in prescribing corrective lenses for patients who are unable to undergo a subjective refraction test that requires a response and judgment from the examinee. Retinoscopic measurement is also used to evaluate the accommodative ability of the eye and to detect latent hyperopia. Although the retinoscope optical structure is simple, the thorough analysis is not easy due to frequent utilization of low-cost, imprecise optical elements. As a consequence of the absence of detailed analysis, medical textbooks illustrate retinoscopy with over-simplified portraits. Further, ambiguous observations occur when the ocular aberrations are significant and when overlap occurs in the light path of the multiple aperture stops, including eye pupils. These limitations and difficulties have perhaps discouraged the quantitative of retinoscopy in clinical practice.
With eye modeling, retinoscopic measurements can be predicted through simulation. The optical layout of the simulation is shown in Fig. 13.7. Using a program such as ZEMAX, light rays from the filament source of retinoscope are traced through the scope and enter the targeted eye model. In the return path, the retinal image at the end of the first ray tracing serves as the light-source object, and the light rays are traced through the peephole of the scope and imaged on the retinal plane of the observer. The eye model of the retinoscopist can be a perfect thin lens with an image
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Fig. 13.7. Optical layout in the retinoscopy simulation.
plane. As in the real condition, four effective apertures were involved in this retinoscopic simulation. These apertures were the small apertures in front of the filament, the window on the beam splitter (along both paths), the pupil of the eye (along both paths), and the peephole of the observation. A coordinate break in the ZEMAX program is required to simulate the movement of the streak projection, as it moves across the examinee’s pupil. Ray aiming is necessary to ensure that all of the vignetting or cut-off effects are encountered when using coordinate breaks.
Figure 13.8 demonstrates some example results of the retinoscopy simulations with eye models. On the left of the figure is the famous scissors reflex of a KC eye, as the streak retinoscope projection moves along the 45-degree meridian. Typically, 100 million rays are traced in each of these double-path simulations to produce one reflex image. A KC patient’s topography and manifest refractive prescription are used to construct a personal eye model. Similar simulations can be performed to predict the observation when the eye is focused on a different direction and when the eye pupils are dilated to different sizes. Likewise, the retinoscope simulation can be assigned to move along any meridian with any sleeve position of the scope. In Ref. 24, both planeand concave-mirror practices of retinoscopy are simulated. The typical ammetropia reflex movements of withand against-motion, and the
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Fig. 13.8. Simulations of retinoscopic observations on (a) a 38-year-old Caucasian female patient’s KC eye and (b) an eye with high degree of SA resulting from a surgical procedure. Scissors reflex and hourglass reflex are illustrated, respectively.
so-called “anomalous with-motion” of the high myopia condition are produced. This type of simulation can be applied to medical training, without the need for using human subjects.
A second retinoscopic example is the hourglass reflex that is shown in Fig. 13.8(b). In the recent years, case observations have suggested that inadvertently induced SAs from surgical procedures, such as the Schachar’s sclera band procedure and the use of intraocular lenses produce “pseudoaccommodation” in presbyopia patient vision. One such case was reported by Dr. Guyton in Johns Hopkins.25,26 Dr. Guyton had the opportunity to examine a patient after surgery for presbyopia. The patient had relied on reading glasses to see objects that were nearby and had undergone Schachar’s scleral band procedures for presbyopia. Two to three months later, she went without glasses entirely, with 20/20 uncorrected VA for viewing both objects in the distance and objects nearby. However, in the dynamic retinoscopic reflex examination, Guyton observed the static hourglass shape of reflex, rather than the streak reflex, for both near and distant visions, thereby demonstrating the absence of actual accommodation. He suspected that the hourglass reflex indicated a condition of a high-degree of SA, which provides an effect of long focal depth, or the so-called “pseudoaccommodatrion.” This hourglass reflex observation can be reproduced with the retinoscopy simulation using general eye models.
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