- •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
Index
action potential, 13–16, 18, 20, 23 adaptive, 47–49
advanced encryption standard, 321 ambient temperature, 357, 358 amblyopia, 101, 117–119 anesthesia, 148, 165, 172 angiography, 280, 282 angle-closure glaucoma, 208, 209 ANN, 301, 311, 312
ANOVA, 159, 160
anterior, 382–384, 387, 392–401, 407, 411 anterior chamber depth, 395, 398, 399 artifacts, 40, 42, 57–60, 80
artificial intelligence, 209 asymmetry, 227, 246, 249–252, 261 automated, 104
backpropagation, 310, 311
BCH code, 323, 329, 331, 332, 336–340, 345, 346
binary image, 54, 55, 57, 59, 63 bioheat, 227, 229, 233, 236 bird flu, 228
bit error rate, 323 blepharitis, 101, 110, 113 blood pressure, 148, 172
blood vessel, 207, 209–211, 213–217, 221, 223, 224
blurring, 43
boundary, 40, 53–58, 60, 64, 65, 71, 72 BW morph, 107
Canny edge, 56, 57 capillary system, 4 carotid artery stenosis, 189
cataract, 93, 96, 97, 101, 106, 107, 110, 114
central nervous system, 3
central retinal vein occlusion, 273 centroid, 71, 75
charged coupled device, 135 choroidal melanoma, 230, 231, 250 classifier, 40
clustering, 99, 100, 110, 112, 113 coagulation, 353, 364–369, 372–375 coefficients, 48, 54, 77, 78, 80, 82 compactness, 72
compression, 320, 322, 323, 326–328, 336, 338, 345, 346
conductivity, 233, 235–237, 244, 245, 248, 252, 254, 260
cones, 5–9, 20–25 confocal, 127
contrast, 40–42, 44–51, 62, 73, 74, 303, 307–310, 312
convection, 233–235, 251–256, 259 convolution, 47, 48, 56, 59, 69 cornea, 2–5, 7, 8
cortical, 9, 10, 24, 27–29, 32–35 cotton-wool spots, 281 cryptographic, 320, 321, 324 cup area, 210, 211, 213, 214
cyclic guanidine monophosphate, 20
455
Index
data mining, 99, 101 decision tree, 92–98, 116
decoder, 328, 334, 335, 338, 341–344 decrypt, 324
deform, 196 depolarization, 15, 17, 20 diabetes retinopathy, 39, 71
diagnostic, 89, 90, 92, 98, 113, 117, 119 dilation, 42, 59, 60, 68
disc-shaped, 213, 214
discrete cosine transforms, 321 discrete Fourier transform, 321 doppler imaging, 273
dry eyes, 101, 110
emissivity, 357, 358 emmetropization, 381 encryption, 319, 321, 323, 324
enhancement, 41, 45, 47–49, 52, 58–60 entropy, 370, 371
erosion, 59, 60
error control codes, 319, 320, 323 Euclidean, 106, 108, 115
euler number, 71
evaporation rate, 235, 251–253, 255, 256, 259
evidence-based medicine, 90 exudates, 301, 303, 306–308, 310,
312–315
eye, 1–9, 21, 26–28
eye tumor, 227–230, 232–237, 239–257, 259–261
false negative, 222 false positive, 222
feature, 39–42, 44–46, 51, 53, 57, 59–61, 64–66, 70, 71, 73–80, 82–84
film grain, 99
fluorescein, 279, 280, 282–285, 287, 290, 292–295
Fourier descriptors, 77, 78 fovea, 7, 8, 21, 22 fundus, 39, 40, 43, 45, 46 fuzzy C-means, 303
Gabor filter, 304
ganglion cells, 5–10, 22–27
Gaussian, 48, 49, 55–58, 68, 69, 79, 80, 83 Gaussian mixture model, 207
gaussian noise, 99
genetic algorithm, 192, 195, 196, 200 glaucoma, 101, 108–112, 114 gradient, 53, 54, 57–59, 67, 84 gradient vector flow, 192 gradient-based, 287
graphical user interface, 98, 101
Hamming code, 329–331, 336–340, 345, 346
Harilick, 74 healthcare, 303 heat loss, 357, 358 hemodynamics, 273
hemorrhages, 301, 303, 306–310, 312, 314, 315
histogram, 49–51 homogeneity, 107 Hough transform, 60–62 hue, 45, 46
Huffman, 323, 326–328, 336, 338 hyperspectral, 123, 124, 133–138,
140–148, 151–159, 174
image processing, 2, 33
image registration, 280, 283, 291, 293, 295 imager, 267, 272–274
infrared, 187 interleaving, 338 interpolation, 199
intraocular pressure, 124, 134, 141, 161–165, 168
invariant moments, 75, 76 ion, 11–18
IOP, 124, 134, 136, 138, 140, 141, 143–149, 151–153, 155, 156, 158–173
keratoconus, 401 keratometer, 383
kernel, 47–49, 54–58, 68, 69, 82, 83
456
Laplacian, 54–56, 83, 289 Laser-Thermokeratoplasty, 349 least significant bit, 321
lens thickness, 395, 400
linear discriminant analysis, 438, 442 localization, 40, 45, 53, 71, 79, 84 lossless, 326–328, 336
macular degeneration, 39 magnetic resonance imaging, 29 Mahalanobis distance, 44 mask, 48, 54, 59, 69
maximum likelihood, 218 median filtering, 42 membrane, 12–18, 20, 21 metastasis, 230 metastasize, 230 microaneurysm, 40, 60, 71 microarray, 125
minima, 202
modeling, 381–386, 389–394, 398–402, 404, 405, 407, 408, 414
moment, 72, 73, 75–77, 82 myopia, 399, 410
Naïve Bayes, 101, 116 neovascularization, 125 nerve fiber layer, 124
neural network, 101–104, 107 neuro-fuzzy, 209
neuron, 3, 5–7, 9–20, 22, 27, 28, 32, 33 neuropathy, 123
neurotransmitter, 11, 17, 18 nonlinear, 42, 43, 45 nonproliferative, 281, 283 normalization, 40, 41, 45, 51, 64, 78 NPDR, 301–305, 312–315 numerical model, 239
ocular melanoma, 230
ocular surface temperature, 188 operators, 54–56
ophthalmology, 89, 98–100, 102, 108 ophthalmoscope, 127
optic nerve, 3, 5–9
Index
optic nerve head, 123, 134–146, 152–157, 159, 161–165, 168, 176–178
optical coherence tomography, 98 optical opacity, 407
optimization, 227, 229, 251, 252, 256, 261 oxygen, 123–149, 151–157, 159–179
pathological, 39, 40, 53 PDR, 301, 303, 305, 312–315 peak-to-valley, 391
perfusion rate, 228, 229, 233–237, 244, 245, 248, 250, 252, 254, 260
perturbation, 124, 147, 148 phosphorescence, 125, 126 photocoagulation, 125 photoreceptor cells, 3 pigment, 4, 6, 9, 20, 21 pigmentation, 41, 45 Planck constant, 370, 371
posterior, 382–384, 392, 399–401 potassium, 12
preprocessing, 39–41, 45, 47, 49, 51, 53, 84
primary open-angle glaucoma, 208 principal component analysis, 78 probability, 93–95, 97, 98, 108 probability density function, 45, 51 pupil, 382, 384, 386, 388, 391, 396, 397,
399, 402–405, 408, 409, 411–413 pupillary axis, 231, 239, 241–244,
246–249
Purkinje images, 383, 384
quantization noise, 99
radial distance, 352 radiation, 265, 267, 268, 275
receiver operating characteristic, 210 recognition, 1, 25, 32, 35
recursive, 66
redundancy, 322, 326–328, 330 refractive power, 349, 350 region of interest, 44, 53, 70, 73 region-growing, 65, 66
relative oxygen saturation, 125, 136–138
457
Index
retina, 3–10, 19, 21–25, 28, 33 retinal detachment, 101 rheumatism, 228
rods, 5, 6, 8, 9, 20–24 rosacea, 101, 110
RS code, 323, 329, 334–340, 344–347
SARS, 228
segmentation, 40–42, 46, 48, 51, 53, 54, 57–71, 84
semi-automated, 190 sensitivity, 207, 210, 222, 224 signal to noise ratio, 257
simulation, 229, 236, 250, 254, 260 Sjögren, 101, 110
slit-lamp, 98, 101, 114 smoothing, 43, 47–49, 54–57
snake, 187, 190, 192, 193, 196–199, 202, 203
sodium, 12
spatial resolution, 4, 10, 22, 26 specificity, 207, 210, 222, 224 Stefan-Boltzmann constant, 357, 358 Subband, 327
support vector machine, 303 SVM, 101, 116
symbol error rate, 323
synapse, 6–10, 13, 16–19, 22, 23, 26
target-tracing, 187, 190, 192, 195–197, 200, 202–204
tear film, 406, 407
telehealth, 417, 421–425, 427, 444, 445, 447
texture, 70, 73, 74, 80, 82, 83, 301, 310, 314, 315
thermogram, 187–193, 198–200, 203 thermography, 228, 250–252, 261
thermometry, 228 three-dimensional, 2
threshold, 44, 54, 57, 58, 62–65, 69 tonometry, 209
top-hat transform, 283, 286, 295 topography, 383, 392, 394, 395, 409, 413 training, 40, 79
transmission, 319–323, 328–330, 334, 336, 338, 340, 341, 344–346
true negative, 222 true positive, 222
turbo code, 323, 329, 335, 336, 340–342, 344–347
ultrasound, 383, 394
universal gas constant, 370, 371
value-based medicine, 90 variance, 428, 446
variance projection function, 203 vascular tree, 431–433, 435–438, 447 vasculature, 280, 283
vision, 1–8, 11, 13, 19, 21, 24, 26, 29–34 visual acuity, 384, 402
visual zone, 387, 388
watermarking, 319–321, 323–325, 336, 338, 344, 346
watershed transform, 68
wavefront, 381, 384, 390, 391, 396, 397, 407
wavelength, 77 wavelet, 80–82, 84 weight, 94–98 Wiener filtering, 310
Zernike polynomial, 390, 397
458
