- •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
Ooi, E.H. and Ng, E.Y.K.
Fig. 12.5. The corneal temperature along the pupillary axis (against z at r = 0) at the end of each laser pulse.
maximum temperature achieved by the corneal endothelium is smaller than 65◦C; implying an absence of endothelial cell deaths.
The spatial temperature profiles over a small cross-section of the cornea and the anterior chamber on the rz plane are shown in Fig. 12.6. The crosssection is selected in such a way that significant variation in the temperature can be observed. For a clearer visualization, the temperature plots in Fig. 12.6 are given with the mirror images about the z-axis.
There appears to be a large temperature gradient at approximately z = 0.5 mm. This may be attributed to the penetration depth of the laser inside the cornea, which according to Eq. (12.4) and based on the properties of the pulsed laser given in Table 12.3, is calculated to be 0.5 mm. Temperature increase at regions defined by z < 0.5 mm is, thus, caused by the absorption of laser energy, while temperature increase at z > 0.5 mm is due to the diffusion of heat from the regions at z < 0.5 mm.
12.7.2. Continuous-Wave Laser
The transient temperature changes inside the eye during the treatment of LTKP using a continuous-wave laser are examined in this section. Both the 10-s coagulation and the minute coagulation (see Sec. 12.2) are investigated. Parameters of the continuous-wave laser used in a typical treatment of LTKP are tabulated in Table 12.4, which are obtained from Brinkmann et al.10 Wavelengths of the lasers used to treat LTKP using continuous-wave lasers
364
Temperature Changes Inside the Human Eye During LTKP
Fig. 12.6. Spatial temperature profiles over a selected cross-section of the eye subject to pulsed laser irradiation.
Table 12.4. Typical laser parameters chosen for the continuous-wave laser.
Parameter |
10-s coagulation |
Minute coagulation |
|
|
|
Laser power, P (W) |
0.125 |
0.10 |
Peak irradiance, Eo (Wm−2) |
4.42 × 105 |
3.54 × 105 |
Heating duration, t(s) |
10 |
60 |
Time step, t(s) |
0.1 |
0.5 |
Wavelength (µm) |
1.87 |
1.87 |
Laser absorption coefficient, µ(m−1) |
1900 |
1900 |
may vary between 1.85 and 1.87 µm. However, in the present study, only the laser that is emitted at a wavelength of 1.87 µm is considered.10 Values of peak irradiance, Eo, are obtained using the expression:
P
Eo = πw2 ,
365
Ooi, E.H. and Ng, E.Y.K.
where P is laser power. The expressions of ψ(t) for the 10-s and minute coagulations are given by:
(t) |
= |
|
0, |
if t > 10 s |
(12.14) |
|
|
|
|
1, |
if t ≤ 10 s |
||
and |
= |
|
|
|
|
|
|
0, |
if t > 60 s |
|
|||
(t) |
|
|
1, |
if t ≤ 60 s , |
(12.15) |
|
respectively. When executing the time stepping scheme, a value of t = 0.1 s is chosen for the 10-s coagulation while for the minute coagulation, a value of t = 0.5 is selected.
Figures 12.7 and 12.8 illustrate the transient temperature profiles along the pupillary axis (r = 0) at various depths of the cornea during the treatment of LTKP for the 10-s and minute coagulations, respectively. Both approaches show uniform increases in the corneal temperature, which are unlike the temperature profiles observed in Fig. 12.4 during pulsed laser radiation. At the end of the laser treatment, a sharp decrease in temperature is found because of the rapid dissipation of heat to the environment and adjacent regions inside the human eye.
The largest temperature reached inside the cornea during the 10-s and minute coagulations are 75.6◦C and 70.1◦C, respectively. The higher
Fig. 12.7. Transient temperature profiles at various depths of the cornea in the 10-s coagulation.
366
Temperature Changes Inside the Human Eye During LTKP
Fig. 12.8. Transient temperature profiles at various depths of the cornea in the minute coagulation.
temperature observed in the 10-s coagulation may be attributed to laser power, which is higher than the laser power used in minute coagulation (0.125 vs. 0.10 W). A large portion of the corneal stroma, defined by depths between 50 and 550 µm, are heated beyond the threshold for corneal shrinkages. This heating is true for both the 10-s and minute coagulations. In both coagulations, no corneal relaxation occurs, since the largest temperatures achieved are not greater than 90◦C.
Figures 12.9 and 12.10 correspondingly show the temperature profiles along the pupillary axis (r = 0) of the cornea at various time levels for both the 10-s and minute coagulations. During the course of radiation, temperatures at the endothelium (z > 550 µm) are found to be less than 55◦C for both the 10-s and minute coagulations. These observations imply that no endothelial cell deaths occur during continuous-wave laser radiation.
The spatial temperature distribution over a small cross-section of the cornea and anterior chamber at t = 2, 4, 6, 8, and 10-s for the 10-s coagulation and at t = 12, 24, 36, 48, and 60 s for the minute coagulation are illustrated in Figs. 10.11 and 10.12, respectively. The temperature plots in Figs. 10.11 and 10.12 have been presented with the mirror images throughout the z-axis. The isotherms observed in Figs. 12.11 and 12.12 appear to
367
Ooi, E.H. and Ng, E.Y.K.
Fig. 12.9. Temperature distribution inside the cornea at various time levels in the 10-s coagulation.
Fig. 12.10. Temperature distribution inside the cornea at various time levels in the minute coagulation.
be more uniformly distributed when compared to the isotherms observed during pulsed laser radiation. A large thermal gradient is observed at about z = 0.52 mm, which is approximately the point defined by the penetration depth of the continuous-wave laser.
368
Temperature Changes Inside the Human Eye During LTKP
Fig. 12.11. Spatial temperature profiles over a selected cross-section of the eye subject to 10-s coagulation radiation.
Fig. 12.12. Spatial temperature profiles over a selected cross-section of the eye subject to minute coagulation radiation.
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