- •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
Hilary W. Thompson
neuron membrane. As the voltage-gated Na+ ion channel becomes inactivated, another channel, the voltage-gated K+ channel, opens and takes the control of the membrane potential, pushing it in turn back downwards toward the equilibrium potential of K+. The K+ voltage-controlled channel in turn is inactivated, and a slight undershoot at the end of the action potential occurs due to a brief voltage-controlled opening of Cl− ion channels. The result is a return to the membrane resting potential. All of these events have electrical analogies, the initial unequal concentrations of ions across the resting membrane act like batteries with potential levels and polarities specific to the distribution of the particular ions in the resting membrane potential. The lipid bilayer and proteins of the neuron membrane act as a capacitor, storing the charges, and act as resistors reducing the ability of charge to cross the membrane. All of these ionic membrane events lead to an equivalent circuit that physically or computationally can be used to model neuron membranes and neuron signaling.
The speed of axonal conduction via action potentials is important in motor evocation of escape mechanisms when sensory inputs signal danger. In invertebrates, the greater speed of axonal conduction is accomplished by increasing axon diameter, resulting in “giant axons” that are involved in sensory to motor system connections. Increases of axonal conduction speed in vertebrates are accomplished in another manner, insulation. Insulation of axons is via specialized glial cell types that wind around axons forming a multilayer myelin sheath. This sheath is interrupted by “nodes” or areas in the myelin sheath that expose the neuron membrane. In these nodal regions, the concentration of voltage-gated Na+ channels is very high, and the action potential is speeded up by “saltatory” or jumping conduction, where rather than separately activating each patch of membrane along the axon, only the nodes need to generate action potentials, which in turn jump to the next node.15
1.4. Synapses
Synapses are the points at which individual neurons communicate with other neurons to provide the inputs and outputs for neurons. The communication between neurons in neuronal circuits is through the actions of synapses, and synaptic actions have a great deal of flexibility to modulate information
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The Biological and Computational Bases of Vision
flow between neurons. At the synapse, the signal transfer functions of connections between neurons are altered in a way that shapes information transmission in the nervous system. Synapses and changes in synaptic efficacy are an important part of the mechanisms by which memories and other information are encoded and stored in the CNS. The plasticity that synapses impart to neuronal circuits allows learning, conditioned responses, and makes them important targets for drug development for neurological and mental diseases.
Synapses are characterized by distinctive anatomical and cellular structures that reflect the capacity of the neurons for manifold variations in communication and control, integration, and local processing of information. They are of two basic types, electrical and chemical; with electrical synapses providing direct coupling of neurons and chemical synapses acting over a synaptic cleft, a space that chemical neurotransmitters diffuse over to act on post-synaptic neurons. Synapses utilize the main ionic currentcontrol mechanisms described for signaling within neurons. These include voltage-controlled ion channels that respond to and integrate incoming signals and establish outgoing signals, and initial differential ion concentrations inside and outside the membrane, resulting in a resting potential, and resting channels that allow ions to move down an electrical and concentration gradient to maintain and reestablish the resting potential. In addition, chemical synapses have novel mechanisms that allow the amplification and control of the time duration of neuronal signaling, as well as enhance the number of possible neuron response types and response flexibility.
Electrical synapses are connections between nerve cells that represent a physical connectivity of the cytoplasm within one neuron to the neuron with which it electrically synapses. The function of electrical synapses was first described in crayfish.16 The connections of the neuronal membranes are made with adhesion structures called gap junctions. Channels made of proteins called connexins17 form these gap junctions. Connexins form a partial channel on each side of the membrane and have subunits that match up to form the neuron-to-neuron channel. Connexin subunits can rotate with respect to each other, and can close off the connection, which may limit damage after trauma to the neurons.
Chemical synapses operate over a measurable gap between neurons. Depolarization of a synaptic ending in a chemical synapse initiates release
17
Hilary W. Thompson
of membrane contained vesicles containing a neurotransmitter substance. The vesicles release their contents by fusing with the presynaptic membrane, and the neurotransmitter substance diffuses across the small gap between the preand postsynaptic cells, binds to postsynaptic receptors, which in turn affect a neuronal signal in the postsynaptic neuron by acting to induce ionic currents and causing postsynaptic potentials. Neurotransmitter substances are mostly small molecules such as the amino acids l-glutamate, arginine, or glycine, but include other molecules such as gamma aminobutyric acid (GABA), or acetylcholine. Peptides can be neurotransmitters as well, including such substances as vasoactive intestinal peptide, or calcitonin gene-related peptide and many others.18 Peptide neurotransmitters differ from small molecule neurotransmitters in their metabolism, actions and release mechanisms. Inhibitory synapses in the CNS often involve GABA or glycine. These inhibitory actions depend on receptor channels in which the binding of the neurotransmitter molecules directly causes a change in the channel conductance, conducting Cl− ions into the cell, which depolarize the postsynaptic cell, making it less likely to fire an action potential. Excitatory synapses involve glutamate-gated ion channels that conduct Na+ and K+ ions. An influx of positively charged ions depolarizes the membrane and makes generation (firing) of an action potential more likely. There are synapses in the retinal neural circuits that represent exceptions to GABA and glycine as inhibition-related neurotransmitters. In fact, it is not the transmitter per se that is inhibitory or excitatory, but rather the kinds of channels, which the binding of the neurotransmitter activates on the postsynaptic membrane. In addition, other modulatory substances that are in the extracellular milieu of the synaptic regions, but which are not neurotransmitters released from presynaptic endings, are present in the nervous system. Receptors for these substances were discovered by the study of the action of neurotoxic substances and named for them, and the molecules that bind these modulatory sites on synapses discovered later. For example, synapses that use glutamate as a neurotransmitter are all excitatory but fall into several classes based on these other molecules. Thus, we can classify glutamate receptors as those that directly gate ion channels and those that invoke a second messenger system to activate ion channels indirectly. The direct acting glutamate receptors are classified as AMPA, kainite, and NMDA types (alpha amino-3-hydroxy-5-methylisoxazole-4-proprionic acid, kainite, and
18
The Biological and Computational Bases of Vision
N-methyl-d-aspartate). These molecules are the toxic agents that allowed discovery of these synaptic subtypes.8
There is increasing knowledge from ongoing research on the properties of inhibitory and excitatory synapses, the drugs that affect them and the diseases that may involve alterations in the function of synaptic physiology. The present review will not address any further detail on the pharmacology or pathology of synaptic function.
The preand postsynaptic elements of communicating nerve cells can be of vastly different sizes. This size difference allows for further complexity of integration of neuronal signals in situations where a number of small presynaptic elements contact a much larger postsynaptic neuron. Inhibitory and excitatory synaptic inputs undergo spatial and temporal integrations, giving rise to enormous numbers of possible states for the neuron-to-neuron communication effected by synapses.
1.5. Vision — Sensory Transduction
All the information we have concerning the state of the world outside our bodies, as well as the position of our bodies in space, and the forces acting on and within our body, are due to the activity of neurons in sensory systems. Thus, all of the wonder of the world we experience, light, sound, touch, taste, and smell, all arise through neuronal processes that begin with the transduction of environmental energy into neuronal signaling, and subsequent processing of the signals from primary sensory neurons in other neuronal circuits. Survival dictates that these representations of the outside world be largely accurate. Food, threats, shelter, and other members of our and other species must all be located accurately in the visual space for us to interact with them successfully.
In the case of vision, photon energy is transduced into neuronal activity in a quantitative manner that informs the nervous system about the luminance, spectral differences, orientation, and motion of light energy that impinges on the photoreceptors of the retina. A long pathway of discovery in neuroscience led to our current detailed molecular level understanding of the beginning of the process of seeing: the mechanism of transduction of the energy of photons into neural signals.
19
Hilary W. Thompson
Rods are elongate cells that are comprised of an outer segment connected to an inner segment by a thin ciliary process, subsequently attached to the cell body and synaptic outputs by a narrow waist. Cones are similar in structure with differences in shape (hence, the names rod and cone), and in the details of the organization of the membrane and photoreceptor protein apparatus. In broad detail, cones and rods are similar, and we will describe the events of phototransduction in the rod where transduction was first explored. Rod outer segments are filled with photoreceptor discs, organelles that concentrate and manage the photoreceptive pigment and protein and the associated signaling pathways.
In the dark, rods are depolarized.19 This depolarization is the opposite of the physiology of excitation in most sensory neurons, where a stimulus leads to the generation of an action potential and subsequent output at the synaptic or output end of the idealized neuron. Thus, sound vibrations excite hair cells in hearing; chemical binding excites chemical receptors in the smell and taste systems, all producing depolarization and action potentials. However, by this common standard, dark seems like excitation to rods and light an inhibitory input. Light hyperpolarizes rods and cones; in the dark, the receptor is depolarized. The reason for this apparent reversible of the sense of sensory signaling in vertebrate photoreceptors remains unexplained.
The sequence of events that set phototransduction in motion causes the reduction of cyclic guanidine monophosphate (cGMP) when a photon is absorbed by a form of vitamin A (retinol) associated with the protein opsin. This visual pigment called rhodopsin is in the photoreceptor discs. The retinol part of the photopigment absorbs the photon and the photon’s energy causes the transmission of a conformational change in the retinal molecule to the associated protein molecule or opsin. In turn, the altered conformation allows the new conformation of the opsin molecule to activate a series of signaling proteins that reduce the concentration of cGMP in the outer segment, closing the cGMP-gated channels and reducing the inward Na+, Ca++ current.20 The K+ channels are thus able to overcome the influx and drive the membrane potential toward hyperpolarization.
Opsin acts differently depending on the wavelength of the energy absorbed and creates effects that are specific to particular wavelength ranges of photons. This behavior accounts for the wavelength sensitivity of photoreceptors and is the beginning of the mechanism of color sensitivity. Note,
20
