- •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
Temperature Changes Inside the Human Eye During LTKP
cornea to be heated beyond this threshold. The temperature that the cornea is eventually heated to depends on the type of lasers used. Two types of lasers, pulsed and continuous-wave, can be used for LTKP.
12.3. Pulsed Laser
Each laser pulse delivers a large amount of energy at a duration that is usually less than 0.25 s. This output is irregular.19 One of the most commonly used pulsed lasers in the treatment of LTKP is the Ho: YAG laser, which emits a laser beam at a wavelength of 2.1 µm.7,16 Other types of pulsed lasers that have been used in LTKP include the Er: Glass and the Tm: YAG laser. In a typical LTKP procedure using a pulsed laser, seven laser pulses, each with the duration of 200 µs, are applied to the corneal surface at a repetition rate of 5 Hz.12
12.4. Continuous-Wave Laser
Unlike pulse lasers, continuous-wave lasers produce steady and continuous output.19 Radiation is usually carried out at a low-energy rate. Among the type of continuous-wave lasers that have been used in LTKP are laser diodes, CO2 lasers, and CoMgF2 lasers. Laser diodes are usually preferred over CO2 and CoMgF2 lasers due to their tunability. This tunability allows the laser beam to be emitted at different wavelengths, hence, making it a more versatile choice for LTKP. Commonly used wavelengths range from 1.85 to 1.87 µm.9 LTKP treatment using continuous-wave lasers may be carried out using two approaches, the 10-s coagulation, where the cornea is heated continuously for 10 s at a laser power of 0.125 W, and the minute coagulation, where the cornea is heated continuously for 60 s are both carried out using a 0.10-W laser.10
12.5. Mathematical Model
12.5.1. Model Description
Our model of the human eye is developed based on dimensions found in literature.20,21 Figure 12.1 displays the model developed with reference
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Fig. 12.1. Model of the human eye used in the study of LTKP.
to the axisymmetrical coordinate system, Orz. The model comprises six regions, which are the cornea, the anterior chamber, the lens, the posterior chamber, the vitreous, and the sclera. These regions are denoted by R1, R2, R3, R4, R5, and R6, respectively.
The retina and choroid, which are relatively thin when compared to the sclera, have been modeled with the sclera as one homogeneous region.22,23 Similarly, the iris and sclera are treated as one homogeneous region, since they have been found to exhibit the same thermal properties.24 The exterior surfaces of the cornea and sclera are denoted by boundaries C1 and C2, respectively.
12.5.2. Governing Equations
Assuming that blood perfusion and metabolic heat generation inside the eye are negligible, and treating each ocular region as a solid, the transient temperature distribution inside the human eye during LTKP, with reference to the model illustrated in Fig. 10.1, may be described using
ρici |
∂ |
[Ti(r, z, t)] = (κi Ti(r, z, t)) + Si(r, z, t) |
|
|
|
||
∂t |
|
||
|
|
for i = 1, 2, 3, 4, 5 and 6, for t > 0, |
(12.1) |
where ρi, ci, and κi are the density, specific heat, and thermal conductivity of region Ri, respectively, Ti is the temperature distribution in Ri, t is time, and Si is the heat generated inside region Ri due to the absorption of laser
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Temperature Changes Inside the Human Eye During LTKP
Table 12.1. Thermal properties of the human eye.
|
Thermal conductivity, κ |
Density, ρ Specific heat, c |
|
Region |
(Wm−1K−1) |
(kgm−3) |
(Jkg−1K−1) |
Cornea, R1 |
0.5825 |
105026 |
417825 |
Anterior chamber, R2 |
0.5827 |
99624 |
399724 |
Lens, R3 |
0.4028 |
105026 |
300028 |
Posterior chamber, R4 |
0.5827 |
99624 |
399725 |
Vitreous, R5 |
0.6025 |
100025 |
417825 |
Sclera, R6 |
1.0024 |
110024 |
318024 |
|
|
|
|
energy. Each region inside the human eye is assumed to be homogeneous and thermally isotropic. Note that the temperature distribution is now a function of space and time.
Values of the thermal conductivity, density, and specific heat of each ocular region may be found in literature.24−28 These values are tabulated in Table 12.1.
Heat that is generated inside the eye, Si due to the absorption of laser energy may be described using the Beer-Lambert law.19 For the typical range of laser wavelengths used in LTKP (10.85–2.1 µm), Si may be mathematically expressed as:
Si(r, z, t) = |
|
0, |
− |
F)E(r, z) exp( |
− |
µz), |
for i = |
2, 3, 4, 5 and 6 , |
|
|
ψ(t)µ(1 |
|
|
for i |
1 |
=
(12.2)
where µ is the corneal absorption coefficient, which is dependent on the wavelength of the laser (unit of m−1), F is the Fresnel reflectance of the corneal surface, E(r, z) is the incident irradiance at the center of the corneal surface, and ψ(t) is given as:
(t) = |
1, |
if laser is off |
, |
(12.3) |
0, |
if laser is on |
which dictates the time that the laser is being applied onto the cornea. When the laser is off, ψ(t) has a value of zero. Consequently, the value of
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Si becomes zero; implying that no heat generation takes place inside the human eye.
Equation (12.2) implies that the laser beam that is incident on the corneal surface generates heat only inside the cornea. This implication is largely attributed to the penetration depth of laser, cornea dp, and thickness. Penetration depth generally describes the depth inside the ocular media where radiation from the laser approaches zero, as it decreases exponentially from the irradiated surface.19 Mathematically, the penetration depth is given by:
dp = µ−1. |
(12.4) |
The typical laser wavelengths used in the treatment of LTKP (as described in Sec. 12.2) ranges from 1.87 to 2.1 µm. These values correspond to those of the corneal absorption coefficient of 1900–2000 m−1. Using Eq. (12.4), the range of dp corresponding to the range of the corneal absorption coefficient is calculated as 0.48–0.50 mm. This calculation suggests that if a laser with a wavelength of 1.87 µm is applied to the corneal surface, the laser radiation approaches zero at a depth of 0.50 mm from the corneal surface. Similarly, if a laser with a wavelength of 2.1 µm is used, radiation at regions beyond the depth of 0.48 mm from the corneal surface becomes negligible. Since the thickness of the cornea in the present model is approximately 0.588 mm, no radiation is expected in the ocular regions located beyond the cornea.
The mathematical expression of the incident irradiance, E(r, z), in Eq. (12.2) depends on the profile of the laser beam that arrives at the corneal surface. Ideally, the laser beam may be assumed to have a flat profile so that the distribution of irradiance is uniform. In most practical cases, the laser beam has a distribution that follows the profile of a Gaussian-type Equation,19 such as shown in Fig. 12.2. For laser beams that are centered near the center of the corneal surface, (r, z) = (0, 0), the function of E(r, z) for a Gaussian beam profile is mathematically given as:
2r2 |
, |
|
E(r, z) = Eo exp − w2 |
(12.5) |
where Eo denotes the peak irradiance and w denotes the radius of the laser beam where the laser irradiance decreases to exp(−1) times its maximum value.19
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