- •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
Jen-Hong Tan et al.
8.1. Introduction to IR Thermography
IR thermography is a temperature-measuring technique that is neither intrusive nor contact required. Moreover, it does not alter surface temperature. Lawson brought this technology to modern medicine in 1956 and found that the temperature of the skin around breast carcinoma elevated compared to normal breast,1 and hence determined the feasibility of using thermography to study breast lesions.2 This thermography has undoubtedly revolutionized the way we measure temperature over the past 50 years.
This thermal imaging technique has numerous applications, for example, to investigate fluxes of convective heat, to comprehend fluid dynamics on complicated shapes,3 and to monitor environmental issues.4 In the medical field, this technology has been applied to manage neuropathic pain,5,6 diagnose diseases such as breast cancer,7,8 rheumatism,9 fever,10 skin lesion,11 and impotence.12 In ophthalmology, this thermal imaging technique currently are used to study and diagnose ocular diseases.13
Today, several ophthalmic imaging techniques are available to investigate ocular anterior anatomy and physiology such as confocal microscopy, slit lamp biomicroscopy, optical coherence tomography, etc. These techniques often provide detailed anatomical description, enable scientists to better understand and determine ocular diseases. Nevertheless, they are unable to detect certain pathological, physiological changes, especially those changes that are obscured or regions that are unreachable under anatomical examination but possible for thermography to capture.
Figure 8.1 shows an ocular thermal image of a normal subject. This form of a two-dimensional (2D) medical signal has brought in much research on ocular diseases such as unilateral exophthalmos,14 dry eye,15 glaucoma,16 etc. Ocular thermography have also been applied in the diagnosis of vascular neuritis17 and retinoblastoma in children.18 Hence, it is possible for the OST to help diagnose ocular diseases.
Before the introduction of this technology to the field of ophthalmology, workers measured ocular temperature by invasive measuring techniques, such as needle probes. During measurement, OST is often lowered due to the application of topical anesthesia. The needle probe cools the ocular surface during its insertion into the eye.19 This cooling leads to measurement error, especially when the penetration depth is below 40 mm. Furthermore,
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The Study of Ocular Surface Temperature by Infrared Thermography
Fig. 8.1. Typical thermogram of normal eye.
this penetration can be traumatic, inducing greater ocular blood flow and thus altering OST.
With thermography, OST can be captured without the above drawbacks. It remotely measures temperature data of some surfaces and does not alter surface temperature. The acquired thermal data is of higher quality, and, hence, enables investigators to study the ocular surface with greater convenience, control, and accuracy.
8.2. Infrared Thermography and the Measured OST
Generally, IR thermography refers to the temperature measurement of some areas by capturing IR radiated from some surfaces. In ophthalmology, the temperature of the ocular surface is of the great interest. Acquisition of OST through an IR thermal imager is widely practiced, because it is noninvasive. However, as a thermal imager determines a surface temperature according to the amount of IR radiation detected, the opacity and the transmissive properties of various ocular tissues are important. It is possible for a thermal imager to receive radiation of an ocular tissue posterior to the ocular surface if tissues around anterior chamber are transparent to IR.
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