- •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
Hyperspectral Image Analysis for Oxygen Saturation Automated Localization of the Eye
Fig. 4.22. Reproducibility of saturation maps obtained in the same monkey at 10 mmHg from separate HSI recordings on the same day. The position of the ONH has been adjusted to the center of each image. Low-to-high saturation is indicated by the progression through blue–cyan–green–yellow–red. Nasal- to-temporal orientation is left-to-right and inferior-to-superior orientation is top to bottom. Reprinted with permission from Khoobehi, B., Kawano, H., Ning, J., Burgoyne, C.F., Rice, D.A., Khan, F., Thompson, H.W., and Beach, J.M. Oxygen saturation changes in the optic nerve head during acute intraocular pressure elevation in monkeys. In: Manns, F., Soderberg, P.G., and Ho, A. (eds.), Ophthalmic Technologies XIX, Proc of SPIE. 7163, 716320. ©2009 SPIE.
The oxygen saturation maps of ONH structures are shown in Fig. 4.23 for increasing IOP. At 30 mmHg, the veins and the ONH areas show slightly decreased saturation, while at 45 mmHg the ONH tissue saturation is substantially reduced. At 55 mmHg, a reduction in the artery is most marked and ONH tissue saturation is further reduced in the rim. The effect of IOP on the oxygen saturations visualized in the saturation maps is in agreement with the percentage saturations in Table 4.5. A linear relationship between numerical values of the saturation map and percentage saturation values obtained by our procedure was established previously.73 No differences in oxygen saturation levels were seen at 10 and 30 min after IOP level adjustment at any of the IOPs evaluated.
4.4.3. Discussion
Percentage oxygen saturation in retinal vessels has previously been reported using vessel oximetry in humans.74,75 Our values from the monkey retinal vessels at the normal IOP are 10–15 percentage points lower than those that had been previously reported. In the present study, deep anesthesia was needed to maintain a stable eye during HSI collection and IOP elevation. It is likely that under deep anesthesia, systemic saturation could have been lower because of less efficient respiration. As with vessels, the tissue values we report may also be lower than would otherwise be found in animals that were awake.
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We previously reported the changes in the oxygen saturation distribution that resulted from experimentally raising IOP from a normal pressure (15 mmHg) to near the perfusion pressure (60 mmHg) for several minutes.33 Sustained high IOP produced dramatic reductions in blood saturation from the ONH tissue and overlying retinal arteries and veins, overriding the autoregulatory control of blood flow in all of these structures. Autoregulatory responses were observed, however, from the saturation rebound at the cup of the ONH. In the present study, we were interested in determining thresholds for the loss of autoregulation in these different structures resulting from the reduced oxygen saturation that occurred in response to graded increases in IOP.
The findings of this study in vessels were (1) elevation of the IOP to 30–45 mmHg did not result in a significant change in the retinal arterial saturation, whereas further elevation to 55 mmHg caused the saturation of arterial blood to decrease significantly; (2) the same stepwise increases in IOP caused approximately equal reductions in the retinal venous saturation at each step above normal IOP; and (3) between 10 and 45 mmHg where autoregulatory responses were effective, the overall decrease in saturation was larger in the veins than in the arteries, causing the retinal arterio-venous saturation difference (A-V difference) to increase with IOP. The additional oxygen extraction from the vein is consistent with a reduced flow resulting from dropping perfusion pressure. The retinal venous saturation is closely tied to the end-capillary blood saturation of the tissue microcirculation; hence, these responses are consistent with compensatory autoregulation of retinal tissue oxygen saturation at elevated IOP, up to 45 mmHg in our experimental model. A blood flow proportional to the perfusion pressure at high IOP was shown in the retinal and prelaminar ONH in monkeys76; in our study, the perfusion pressure dropped to single digits, which would indicate that the blood flow was significantly reduced.
The mechanism by which the retinal arterial saturation is decreased at reduced flow is not known. However, the reports of significant longitudinal gradients in the periarteriolar PO2 in small arteries and arterioles, with essentially no difference between the intraand extravascular tissue oxygen tensions, have been explained by diffusion of oxygen from these precapillary vessels.77 More recently, spectral imaging of the saturation distributions in rat-cremaster vessel networks demonstrated oxygen transfer from arteries to
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Hyperspectral Image Analysis for Oxygen Saturation Automated Localization of the Eye
veins running in parallel, or at crossover points.78 Thus, there is an opportunity for oxygen exchange between the central RA and vein, which run together for several millimeters in close proximity, within the distance for oxygen exchange by diffusion, before their entrance from the ONH cup. If oxygen were removed by leakage at a fixed rate, lower volume flow would result in a decrease in the PO2 of the blood supply at the inner retinal arteries. As the dissolved oxygen concentration is reduced, arterial saturation must decrease according to the oxygen dissociation curve. Therefore, the stable relationship we observed between arterial saturation and IOP at 10 and 30 mmHg may reflect autoregulatory control, whereas the nonlinear relationship over the higher range of IOP may, as we speculate, reflect passive flow-dependent mechanisms that could involve oxygen exchange from central RA to retinal vein. There is presently no evidence for this, however.
A relationship between saturation and IOP, which is similar to that found for retinal arteries and veins, was observed in the ONH. However, the blood supply in the ONH is more complex. Our recordings in the ONH rim yielded results similar to those in retinal arteries, showing a stable saturation of the blood supply over the first step in IOP, and reduced saturation at higher IOP. This response is consistent with previous measurements of blood flow changes during acute elevations in IOP in humans.79 Over the lower range of elevated IOP, the absence of change in saturation at 10 and 30 min after raising the IOP suggests that autoregulation of flow in the ONH rim was present. Reduced oxygen saturation in the higher range of elevated IOP is consistent with reduced blood saturation in the blood supply to this tissue. Since this region receives its blood supply via peripapillary retinal arterioles, which are supplied through the central RA, those saturation responses that were observed in the RA could be also present at the ONH rim. The prelaminar blood supply has been shown to be masked by vessels in the NFL in early arterial phases of fluorescein angiograms in monkeys.80 Hence, it is likely that our observations of blood saturation in the ONH rim by reflectometry are confined to the NFL, which shares its blood supply with the peripapillary retina.
Within the ONH cup, particularly the temporal aspect, which is central to the spread of the NFL, we observed a steady series of smaller reductions in the blood saturation at each step above normal IOP, similar to the finding in the retinal vein. Since the NFL circulation does not mask the central
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Fig. 4.23. ONH saturation maps for IOP values of 10 mmHg (top-left), 30 mmHg (top-right), 45 mmHg (bottom-left), and 55 mmHg (bottom-right). Low-to-high saturation is indicated by the progression through blue–cyan–green–yellow–red. Nasal-to-temporal orientation is left-to-right and inferior-to-superior orientation is top to bottom. Reprinted with permission from Khoobehi, B., Kawano, H., Ning, J., Burgoyne, C.F., Rice, D.A., Khan, F., Thompson, H.W., and Beach, J.M. Oxygen saturation changes in the optic nerve head during acute intraocular pressure elevation in monkeys. In: Manns, F., Soderberg, P.G., and Ho, A. (eds.), Ophthalmic Technologies XIX, Proc of SPIE. 7163, 716320. ©2009 SPIE.
cup, the deeper layers of the ONH, including the prelaminar and laminar regions, would be accessible to optical reflectometry at the central cup. The short posterior ciliary arteries and branches from the pial artery supply the blood flow to these layers of the optic nerve; little flow is derived from the central RA.3,4 Fluorescein fundus angiography has also demonstrated a blood supply to these regions from the peripapillary choroid.44 Thus, saturation changes in the temporal cup would not be expected to follow those seen in the rim. Although the relationship between cup saturation and IOP is similar to that of the retinal vein, it may be the result of redistribution of flow from the prelaminar region into the retrolaminar region behind the lamina cribrosa.81 Our recordings of the oxygen saturation levels of
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