- •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
Thomas P. Karnowski et al.
minimal disease). Category 2 validation should be able to accurately determine if sight-threatening DR including any level of macular edema, ETDRS level 53 (or worse), or proliferative DR is present. Category 2 validation distinguishes patients with sight-threatening DR who require prompt referral for possible laser surgery. Category 3 validation applies to a system that can distinguish ETDRS-defined levels of nonproliferative DR (mild, moderate, or severe), proliferative DR (early, high-risk), and macular edema with accuracy sufficient to determine appropriate follow-up and treatment strategies (equivalent to a retinal examination through dilated pupils). Category 4 validation applies to a system that matches or exceeds the ability of the ETDRS photos to determine levels of DR. Ultimately, we believe that the CBIR method has the potential to perform at a Category 3 validation level to stratify and effectively manage DR at an expert level using nonmydriatic images taken in the primary care setting.
Acknowledgments
These studies were supported in part by grants from Oak Ridge National Laboratory, the National Eye Institute (EY017065), the Health Resources and ServicesAdministration, by an unrestricted UTHSC Departmental grant from Research to Prevent Blindness, New York, NY, Fight for Sight, New York, NY, and by The Plough Foundation, Memphis, TN.
References
1.International Diabetes Federation. Available at http://www.idf.org.
2.Klein, R., Klein, B.E., Moss, S.E., Davis, M.D., and DeMets, D.L. The Wisconsin epidemiologic study of diabetic retinopathy. II. Prevalence and risk of diabetic retinopathy when age at diagnosis is less than 30 years. Arch Ophthalmol 102:520–526, 1984.
3.Klein, R., Klein, B.E., Moss, S.E., and Linton, K.L. The beaver dam eye study. Retinopathy in adults with newly discovered and previously diagnosed diabetes mellitus. Ophthalmology 99:58–62, 1992.
4.McGlynn, E.A., Asch, S.M., Adams, J., Keesey, J., Hicks, J., DeCristofaro, A., and Kerr, E.A. The quality of health care delivered to adults in the United States. N Engl J Med 348:2635–2645, 2003.
5.Newcomb, P.A. and Klein, R. Factors associated with compliance following diabetic eye screening. J Diabet Complications 4(1):8–14, 1990.
448
Automating the Diagnosis, Stratification, and Management of DR
6.DRS Report Number 8. The Diabetic Retinopathy Study Research Group. Photocoagulation treatment of proliferative diabetic retinopathy. Clinical application of Diabetic Retinopathy Study (DRS) findings. Ophthalmology 88:583–600, 1981.
7.Early Treatment Diabetic Retinopathy Study Report Number 1. Photocoagulation for diabetic macular edema: Arch. Ophthalmol 103:1796–1806, 1985.
8.Age-Related Eye Disease Study Report Number 3. Risk factors associated with agerelated macular degeneration. A case-control study in the age-related eye disease study. Ophthalmology 107:2224–2232, 2000.
9.Larsen, M., Godt, J., and Grunkin, M. Automated detection of diabetic retinopathy in a fundus photographic screening population. Invest Ophthal Vis Sci 44:767–771, 2003.
10.Teng, T., Lefley, M., and Claremont, D. Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy.
Med Biol Eng Comput 40:2–13, 2001.
11.Foracchia, M., Grisan, E., and Ruggeri, A. Luminosity and contrast normalization in retinal images. Med Image Anal 9:179–190, 2005.
12.Martin-Perez, M.E., Hughes, A.D., Stanton, A.V., et al. Segmentation of retinal blood vessels based on the second directional derivative and region growing. IEEE Inte Conf Image Process 173–176, 1999.
13.Hoover, A., Kouznetsova, V., and Goldbaum, M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19:203–210, 2000.
14.Hoover, A. and Goldbaum, M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imaging 22:951–958, 2003.
15.Foracchia, M., Grisan, E., and Ruggeri, A. Detection of the optic disk in retinal images by means of a geometrical model of vessel structure. IEEE Trans Med Imaging 23:1189–1195, 2004.
16.Abramoff, M.D., Alward, W.L., Greenlee, E.C., Shuba, L., Kim, C.Y., Fingert, J.H., and Kwon, Y.H. Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features. Invest Ophthalmol Vis Sci 48:1665–1673, 2007.
17.Morris, D.T. and Donnison, C. Identifying the neuroretinal rim boundary using dynamic contours. Image Vis Comput 17:169–174, 1999.
18.Li, H. and Chutatape, O. Automated feature extraction in color retinal images by a model based approach. IEEE Trans Biomed Eng 51:246–254, 2004.
19.Tobin, K.W., Karnowski, T.P., Arrowood, L.F. et al. Content-based image retrieval for semiconductor process characterization. EURASIP J Appl Signal Processing 7:704–713, 2002.
20.Karnowski, T.P., Tobin, K.W., Ferrell, R.K., and Lakhani, F. Using an image retrieval system for image data management. Design, Process Integration, and Diagnostics in IC Manufacturing, Proceedings of the SPIE 4692:120. doi:10.1117/12.475648, 2002.
21.Chaum, E., Karnowski, T.P., Govindasamy, V.P., Abdelrahamen M., and Tobin K.W. Automated diagnosis of retinopathy by content-based image retrieval. Retina 28:1463–1477, 2008.
22.Wei, Z., Wu, Y., Deng, R.H., Yu, S., Yao, H., Zhao, Z., Ngoh, L.H., Han, L.T., and Poh, E.W.T. A secure and synthesis tele-ophthalmology system. Telemed E Health 14:833–845, 2008.
449
Thomas P. Karnowski et al.
23.Li, Y., Karnowski, T.P., Tobin, K.W., Giancardo, L., and Chaum, E. A network infrastructure for automated diagnosis of diabetic retinopathy. Proc American Telemedicine Association Annual Meeting, 2009.
24.Cavallerano, A.A., Cavallerano, J.D., Katalinic, P., Tolson, A.M., Aiello, L.P., and Aiello, L.M. JoslinVision Network Clinical Team, Use of JoslinVision Network digitalvideo nonmydriatic retinal imaging to assess diabetic retinopathy in a clinical program. Retina 23:215–223, 2003.
25.Fransen, S.R., Leonard-Martin, T.C., Feuer, W.J., and Hildebrand P.L. Inoveon Health Research Group. Clinical evaluation of patients with diabetic retinopathy: accuracy of the Inoveon diabetic retinopathy-3DT system. Ophthalmology 109:595–601, 2002.
26.Wei, J., Valentino, D., Bell, D., and Baker, R. A web-based telemedicine system for diabetic retinopathy screening using digital fundus photography. Telemed E Health 12:50–57, 2006.
27.Zahlmann, G., Schubert, M., Obermaier, M., and Mann, G. Concept of a knowledge based monitoring system for glaucoma and diabetic retinopathy using a telemedicine approach. Proc. 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Amsterdam, pp. 1230–1231, 1996.
28.Niemeijer, M., Abramoff, M.D., and van Ginneken, B. Information fusion for diabetic retinopathy CAD in digital color fundus photographs. IEEE Trans Med Imaging 28: 775–785, 2009.
29.Wang, L., Bhalerao, A., and Wilson, R. Analysis of retinal vasculature using a multiresolution Hermite model. IEEE Trans Med Imaging 26:137–152, 2007.
30.Hoover, A., Kouznetsova, V., and Goldbaum, M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19:203–210, 2000.
31.Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., and Goldbaum, M. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging 8:263–269, 1989.
32.Jiang, X. and Mojon, D. Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans Pattern Anal Mach Intell 25:131–137, 2003.
33.Soares, J.V.B. et al. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imaging 25:1214–1222, 2006.
34.Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., and Abramoff, M.D. Comparative study of retinal vessel segmentation methods on a new publicly available database. Proc SPIE 5370:648–656, 2004.
35.Niemeijer, M., Abramoff, M.D., and van Ginneken, B. Segmentation of the optic disc, macula and vascular arch in fundus photographs. IEEE Trans Med Imaging 26:116–127, 2007.
36.Zana, F. and Klein, J. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans on Image Processing 10:1010–1019, 2001.
37.Fang, B., Hsu, W., and Lee, M.L. Reconstruction of vascular structures in retinal images. Proc ICIP’03 II:157–160, 2003.
38.Vincent, L. Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans Image Processing 2:176–201, 1993.
450
Automating the Diagnosis, Stratification, and Management of DR
39.Fleming, A.D., Philip, S., Goatman, K.A., Olson, J.A., and Sharp, P.F. Automated assessment of diabetic retinal image quality based on clarity and field definition. Invest Ophthalmol Vis Sci 47:120–1125, 2006.
40.Lalonde, M., Gagnon, L., and Boucher M.C. Automatic visual quality assessment in optical fundus images. Proc Vision Interface, 2001.
41.Niemeijer, M., Abramoff, M.D., and van Ginneken, B. Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening. Med Image Anal 10:888–898, 2006.
42.Usher, D.B., Himaga, M., and Dumskyj, M.J. Automated assessment of digital fundus image quality using detected vessel area. Proc Medical Image Understanding and Analysis, British Machine Vision Association (BMVA), pp. 81–84, 2003.
43.Giancardo, L., Abramoff, M.D., Chaum, E., Karnowski, T.P., Meriaudeau, F., Tobin, K.W. Elliptical local vessel density: a fast and robust quality metric for fundus images.
Conf Proc IEEE Eng Med Biol Soc 1:3534–3537, 2008.
44.Giancardo, L. Quality analysis of retina images for the automatic diagnosis of diabetic retinopathy. Master’s Thesis, Msc Erasmus Mundis in VIBOT, 2008.
45.Youssif, A.-R., Ghalwash, A.Z., and Ghoneim, A.-R. Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Trans Med Imaging 27:11–18, 2008.
46.Hoover, A. and Goldbaum, M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imaging 22: 951–958, 2003.
47.Tobin, K.W., Chaum, E., Govindasamy,V.P., and Karnowski, T.P. Detection of anatomic structures in human retinal imagery. IEEE Trans Med Imaging 26:1729–1739, 2007.
48.Karnowski, T.P., Govindasamy, V.P., Tobin, K.W., and Chaum, E. Locating the optic nerve in retinal images: comparing model-based and Bayesian decision methods. Proc. 28th Annual International Conf. of the IEEE EMBS, 2006.
49.Karnowski, T.P., Aykac, D., Chaum, E., Giancardo, L., Li, Y., Tobin, K.W., and Abràmoff, M.D. Evaluating the accuracy of optic nerve detections in retina imaging by using complementary methods. Proc World Congress of Medical Physics and Biomedical Engineering, Munich, 2009.
50.Karnowski, T.P., Aykac, D., Chaum, E., Giancardo, L., Li, Y., and Tobin, K.W., Jr., Abramoff, M.D. Practical considerations for optic nerve location in telemedicine. Proc 31st Annual International Conf of the IEEE EMBS, 2009.
51.Marquardt, D. An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math 11:431–441, 1963.
52.Huang, D., Kaiser, P., Lowder, C., and Traboulsi, E. Retina Imaging, Elsevier, 2006.
53.Patton, N., Aslam, T.M., MacGillivray, T., Deary, I.J., Dhillon, B., Eikelboom, R.H., Yogesan, K., and Constable, I.J. Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res 25:99–127, 2006.
54.Niemeijer, M., van Ginneken, B., Staal, J., Suttorp-Schulten, M.S., and Abramoff, M.D. Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Med Imaging 24:584–592, 2005.
55.Huang, K. and Yan, M. A local adaptive algorithm for microaneurysms detection in digital fundus images. Proc Computer Vision for Biomedical Image Applications: First International Workshop, Beijing, China, 2005.
451
Thomas P. Karnowski et al.
56.Raman, B., Bursell, E.S.,Wilson, M., Zamora, G., Benche, I., Nemeth, S.C., and Soliz, P. The effects of spatial resolution on an automated diabetic retinopathy screening system’s performance in detecting microaneurysms for diabetic retinopathy. Proc 17th IEEE Symposium on Computer-Based Medical Systems, p. 128, 2004.
57.Hafez, M. and Azeem, S.A. Using adaptive edge technique for detecting microaneurysms in fluorescein angiograms of the ocular fundus. Proc IEEE MELECON 479–483, 2002.
58.Pallawala, P.M.D.S., Hsu, W., Lee, M.L., and Goh, S.S. Automated microaneurysm segmentation and detection using generalized eigenvectors. Seventh IEEE Workshops on Application of Computer Vision, pp. 322–327, 2005.
59.Sbeh, Z.B., Cohen L.D., Mimoun, G., and Coscas, G. A new approach of geodesic reconstruction for drusen segmentation in eye fundus images. IEEE Trans Med Imaging 20:1321–1333, 2001.
60.Osareh, A., Mirmehdi, M., Thomas, B., and Markham, R. Automated identification of diabetic retinal exudates in digital colour images. Br J Ophthalmol 87:1220–1223, 2003.
61.Walter, T., Klein, J.-C., Massin, P., and Erginay, A. A contribution of image processing to the diagnosis of diabetic retinopathy — detection of exudates in color fundus images of the human retina. IEEE Trans Med Imaging 21:1236–1243, 2002.
62.Niemeijer, M., Abramoff, M.D., and van Ginneken B. Fast detection of the optic disc and fovea in color fundus photographs. Med Image Anal 13:859–870, 2009.
63.Giancardo, L., Meriaudeau, F., Karnowski, T.P., Chaum, E., Tobin, K. W., and Li, Y. Microaneurysms detection with the Radon Cliff Operator in retinal fundus images.
Proceedings of SPIE Medical Imaging, San Diego, 2010.
64.Giancardo, L., Chaum, E., Karnowski, T.P., Meriaudeau, F., Tobin, K.W., and Li, Y. Bright retinal lesions detection using color fundus images containing reflective features. Proceedings of World Congress of Medical Physics and Biomedical Engineering, Munich, 2009.
65.Duda, R.O., Hart, P.E., and Stork, D.G. Pattern Classification. Second Edition, John Wiley and Sons, Inc., New York, 2001.
66.Tobin, K.W., Abdelrahman, M., Chaum, E., Govindasamy, V.P., and Karnowski, T.P. A probabilistic framework for content-based diagnosis of retinal disease. Proc 29th Annual International Conf of the IEEE EMBS, Lyon, France, 2007.
67.Tobin, K.W., Abramoff, M.D., Chaum, E., Giancardo, L., Govindasamy, V.P., Karnowski, T.P., and Tennant, M.T.S. Using a patient image archive to diagnose retinopathy. Proc 30th Annual International Conf of the IEEE EMBS, Vancouver, Canada, 2008.
68.Abramoff, M.D. and Suttorp-Schulten, M. Web-based screening for diabetic retinopathy in a primary care population: the eyecheck project, Telemed E Health 11:668–674, 2005.
69.Abràmoff M.D., Niemeijer, M., Suttorp-Schulten, M.S., Viergever, M.A., Russell, S.R., and van Ginneken, B. Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care 31:193–198, 2008.
70.Rudnisky, C.J., Tennant, M.T.S., Weis, E., Ting, A., Hinz, B.J., and Greve, M.D.J. Webbased grading of compressed stereoscopic digital photography versus standard slide
452
Automating the Diagnosis, Stratification, and Management of DR
film photography for the diagnosis of diabetic retinopathy. Ophthalmology 114:1748– 1754, 2007.
71.American Telemedicine Association, Ocular Telehealth Special Interest Group, and the National Institute of Standards and Technology Working Group. Telehealth Practice Recommendations for Diabetic Retinopathy. Telemed J E Health 10:469–482, 2004.
453
This page intentionally left blank