- •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.
abnormal ocular IR thermal images are used, and the proposed algorithm localizes the eye and cornea accurately in 84% of all 269 IR thermograms.
The most common causes of blindness around the world, according to an estimation released by the World Health Organization (WHO) in 2002, are cataracts (47.8%), glaucoma (12.3%), age-related macular degeneration (AMD) (8.7%), trachoma (3.6%), corneal opacity (5.1%), and diabetic retinopathy (4.8%) [http://www.owsp.org/problem.htm]. The chances of an individual developing an ocular disease rise with age. Normal vision can become impaired if early and immediate treatment of an eye disease is not received. Among those age-related diseases, cataract is the most common eye abnormality. Cataracts are caused by the clouding of the ocular lens. Iridocyclitis, corneal haze, and dry eye are other commonly seen anterior segment eye abnormalities. The early detection of these ocular impairments and the subsequent proper treatments can prevent vision loss.
5.1. Introduction to Thermography
Thermography is a noninvasive and fast thermal imaging technique. This temperature measuring technology has been applied in thermofluid dynamics,1 polygraph testing,2 environmental monitoring,3 and agriculture.4 Medical field researchers have used it to diagnose breast cancer,5,6 to manage neuropathic pain,7,8 impotence,9 and thyroid gland disease,10 and to record ocular surface temperature (OST), either as a single image or as a sequence, for the studies of ocular physiology and pathology.11
Temperature profiles across the anterior ocular surface were first examined in detail using a wide-field of color-coded thermal imagers in 1989.12 Previously, invasive investigating techniques, such as a needle probe, were the only options to the study of OST. Topical anesthesia is needed during measurement, and discrepancies in temperature reading can be up to 6◦C.13 These methods can be traumatic and can induce unnecessary ocular blood flow.
Hence, a detailed study of ocular diseases was not possible with the invasive investigating techniques. Nor was it possible with early IR thermometry. However, as the technology of thermography advances, with higher precision and resolution, IR thermal imaging can be used to study ocular physiology, such as the relationship between the physical properties of
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the anterior eye and OST14 and the relationship between OST and ocular diseases, with greater ease and accuracy.
The wide field color-coded IR thermal imager revealed physiological correlations between oral temperature and age and OST. Normal individuals with a higher oral temperature showed a higher OST.15 The temperature difference between the central cornea and limbus averaged 0.45◦C.12,15 In the experiment, corneal surface temperature decreased at a value of 0.001◦C per year as test subjects aged,16 and the intraocular difference in corneal surface temperature was less than 0.6◦C for normal subjects.15
Subjects with iridocyclitis illustrated a higher corneal surface temperature under the inspection of thermogram. In some cases, the corneal surface temperature was 2.2◦C higher, where the average temperature for normal subjects was 35.4±0.1◦C.17 For dry eye18 and herpes zoster ophthalmicus,19 the affected eye was observed to be warmer than the unaffected eye.
In primary open-angle glaucoma (POAG), the temperature of the infected eye was lower than the temperature of the healthy eye. The temperature of five anatomical points (the internal canthus, the external canthus, the half-way point between the internal canthus and nasal limbus, the center of the cornea, and the half-way point between the temporal limbus and external canthus) were lower on the thermograms of POAG subjects than on the thermograms of normal individuals.20 Those temperatures were correlated to the resistivity index, and the effect of retrobulbar hemodynamics on OST was highlighted.20 In another examination, those eyes that suffered from ischemic central retinal vein occlusion were said to be cooler than those that did not suffer from ischemic central retinal vein occlusion.21 An investigation in carotid artery stenosis (CAS) revealed a negative correlation between the degree of CAS and the OST.22
The application of IR thermal imaging to diagnose ocular diseases is still in the nascent stage, and has not been as successful in this field, as it has been in diagnosing breast cancer and vascular diseases. The main obstacle for the application of IR thermal imaging is the lack of sophisticated analysis and methods to localize the eye and cornea systematically and automatically.
The cornea is commonly manually located on the IR thermogram, and OST or corneal surface temperature is acquired either through a group of separated points or through points within the region identified as the corneal area.11 The boundary between the iris and sclera is not discernible on the
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thermogram. Therefore, a fully automated localization of the cornea is difficult.
A study by Efron et al. in which a wide field of color-coded IR thermal imagers was first utilized to capture OST, approached the above issue by estimating the geometric center of the cornea and measuring the temperature for every 0.5 mm increments on either side of the ocular anterior surface horizontally. Similar measures were proposed in latter studies, to acquire temperatures of five anatomical locations along the horizontal meridian of the anterior surface.20,21,23 This problem was also approached using one24,25 or five 10 × 10 pixels boxes15,18,22 put along the horizontal meridian, a squared 20 × 20 pixels box,26 or a circular region 6,27 placed at the center of an eye.
As a step further, Acharya et al. developed a semi-automated algorithm to obtain a corneal surface temperature.28 In this method, the thermogram was converted to gray-scale, and manually cropped to include only the eye. The resultant image matrix was resized to a standard 400 × 200 pixels, and the cornea was located, with a radius of one-fourth of the length of the eye.
This study proposed a fully automated algorithm to localize the eye and cornea, and acquire OST without any human intervention. It used the snake algorithm to delineate the boundary of the eye. Snake and other deformable template matching techniques, such as spline-based shape matching, diffusion snake, and active shape model, are local optimizers that require a good initial position for correct localization. In many of the algorithm applications, users are required to determine the initial position and shape for a snake. In this study, given the eye captured may not fall at the center of the thermogram, proper initialization in the snake algorithm is necessary to prevent incorrect localization.
However, user intervention in initialization is not required in this algorithm, nor are users required to monitor the expansion or convergence of the snake. The above issue is solved by the target-tracing function.29 The targettracing function is formulated so that the resultant snake which accurately localizes the boundary of an eye and will give the function the smallest value. A search for minimum on that function is performed in the algorithm over a number of possible converged snakes to deliver the final correct delineation. The corneal center and its radius are obtained based on the final resultant snake points.
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The chapter has been organized as follows: in Sec. 5.2, we present the data acquisition process. In Sec. 5.3, we explain the methods and in Sec. 5.4, we present the results of the systems. The discussion on our data analysis is presented in Sec. 5.5. Finally, we conclude the chapter in Sec. 5.6.
5.2. Data Acquisition
Two sets of thermograms were taken for this study. The first set consisted of 181 thermal images taken from 91 normal subjects aged 14–76 years. These thermograms were collected at The Biomedical Centre, Ngee Ann Polytechnic, Singapore. Before data collection, the subjects were asked to relax for 10 minutes. Participants who had any of the following conditions were excluded:
•an eye disease,
•a history of serious eye disease,
•poor general health, and
•a history of ocular or facial surgery.
The second set consisted of images from 44 subjects who each had at least one eye abnormality. The details of the participant eye health are tabulated in Table 5.1. The data collections were conducted at Clinic E, National University Hospital, Singapore. Participants were not allowed to drink hot liquids, rub their eyes, or use teardrops within 20 minutes before the thermal shooting.
Table 5.1. Recruited subjects in this study.
Cases |
No. of subjects |
Percentage |
|
|
|
Cataract |
4 |
8.8 |
Post-cataract |
5 |
11.1 |
Dry eye syndrome |
5 |
11.1 |
Diabetes and diabetic complication |
9 |
20.0 |
Blepharitis |
3 |
6.6 |
Episcleritis, conjunctivitis |
5 |
11.1 |
Others |
14 |
31.1 |
Total |
44 |
100.0 |
|
|
|
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