Ординатура / Офтальмология / Английские материалы / Automated Image Detection of Retinal Pathology_Jelinek, Cree_2009

million adults in 61 prospective studies, Lancet, 360(9349), 1903, 2002.

[23]Arnold, J.V., Gates, J.W.C., and Taylor, K.M., Possible errors in the measurement of retinal lesions, Invest Ophthalmol Vis Sci, 34, 2576, 1993.

[24]Wong, T.Y., Wang, J.J., Rochtchina, E., et al., Does refractive error influence the association of blood pressure and retinal vessel diameters? The Blue Mountains Eye Study, Am J Ophthalmol, 137, 1050, 2004.

[25]Stokoe, N.L. and Turner, R.W.D., Normal retinal vascular pattern. Arteriovenous ratio as a measure of arterial calibre, Br J Ophthalmol, 50, 21, 1966.

[26]Parr, J.C. and Spears, G.F.S., Mathematic relationships between the width of a retinal artery and the widths of its branches, Am J Ophthalmol, 77, 478, 1974.

[27]Parr, J.C. and Spears, G.F.S., General calibre of the retinal arteries expressed as the equivalent width of the central retinal artery, Am J Ophthalmol, 77, 472, 1974.

[28]Hubbard, L.D., Brothers, R.J., King, W.N., et al., Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study, Ophthalmology, 106, 2269, 1999.

[29]Murray, C.D., The physiological principle of minimum work: the vascular system and the cost of blood volume, Proc Natl Acad Sci USA, 12, 207, 1926.

[30]Murray, C.D., The physiological principle of minimum work applied to the angle of branching of arteries, J Gen Physiol, 9, 835, 1926.

[31]Sherman, T.F., On connecting large vessels to small. The meaning of Murray’s law, J Gen Physiol, 78, 431, 1981.

[32]LaBarbera, M., Principles of design of fluid transport systems in zoology, Science, 249(4972), 992, 1990.

[33]Hutchins, G.M., Miner, M.M., and Boitnott, J.K., Vessel caliber and branch angle of human coronary artery branch points, Circ Res, 38, 572, 1976.

[34]Zamir, M., Optimality principles in arterial branching, J Theor Biol, 62, 227, 1976.

[35]Zamir, M., Nonsymmetrical bifurcations in arterial branching, J Gen Physiol, 72, 837, 1978.

[36]Zamir, M., Medeiros, J.A., and Cunningham, T.K., Arterial bifurcations in the human retina, J Gen Physiol, 74, 537, 1979.

[37]Woldenberg, M.J. and Horsfield, K., Relation of branching angles to optimality for four cost principles, J Theor Biol, 122, 187, 1986.

[38]Griffith, T.M., Edwards, D.H., Davies, R.L., et al., EDRF coordinates the behaviour of vascular resistance vessels, Nature, 329, 442, 1987.

[39]Chapman, N., Baharudin, S., King, L., et al., Acute effects of L-NMMA on the retinal arteriolar circulation in man, J Hum Hypertens, 14, 841, 2000.

[40]McLenachan, J.M., Williams, J.K., Fish, R.D., et al., Loss of flowmediated endothelium-dependent dilation occurs early in the development of atherosclerosis, Circ, 84, 1273, 1991.

[41]Witt, N., Wong, T.Y., Hughes, A.D., et al., Abnormalities of retinal microvascular structure and risk of mortality from ischemic heart disease and stroke, Hypertension, 47, 975, 2006.

[42]Struijker-Boudier, H.A.J., le Noble, J.L.M.L., Messing, M.W.J., et al., The microcirculation and hypertension, J Hypertens, 10(Suppl 7), S147, 1992.

[43]Hart, W.E., Goldbaum, M., Cotˆe,´ B., et al., Measurement and classification of retinal vascular tortuosity, Int J Med Inform, 53, 239, 1999.

[44]Brinchmann-Hansen, O. and Engvold, O., Microphotometry of the blood column and the light streak on retinal vessels in fundus photographs, Acta Opthalmologica, Suppl 179, 9, 1986.

[45]Chapman, N., Witt, N., Gao, X., et al., Computer algorithms for the automated measurement of retinal arteriolar diameters, Br J Ophthalmol, 85, 74, 2001.

[46]Gao, X., Bharath, A.A., Hughes, A.J., et al., Towards retinal vessel parameterization, Proc SPIE, 3034, 734, 1997.

[47]Brinchmann-Hansen, O. and Heier, H., Theoretical relations between light streak characteristics and optical properties of retinal vessels, Acta Opthalmologica, Suppl 179, 33, 1986.

[48]Brinchmann-Hansen, O. and Engvold, O., The light streak on retinal vessels.

I.A theoretical modelling of the reflex width, Acta Opthalmologica, Suppl 179, 38, 1986.

[49]Brinchmann-Hansen, O. and Engvold, O., The light streak on retinal vessels.

II.A theoretical modelling of the reflex intensity, Acta Opthalmologica, Suppl 179, 46, 1986.

[50]Press, W.H., Teukolsky, S.A., Vetterling, W.T., et al., Numerical Recipes in C

— The art of scientific computing, Cambridge University Press, Cambridge, 2nd ed., 1997.

[51]King, L.A., Stanton, A.V., Sever, P.S., et al., Arteriolar length-diameter (L:D) ratio: a geometric parameter of the retinal vasculature diagnostic of hypertension, J Human Hypertens, 10, 417, 1996.

[52]Stanton, A.V., Wasan, B., Cerutti, A., et al., Vascular network changes in the retina with age and hypertension, J Hypertens, 13(12 Pt2), 1724, 1995.

[53]Egashira, K., Inou, T., Hirooka, Y., et al., Effects of age on endotheliumdependent vasodilation of resistance coronary artery by acetylcholine in hu-

mans, Circ, 88, 77, 1993.

[54]Chapman, N., Mohamudally, A., Cerutti, A., et al., Retinal vascular network architecture in low-birth-weight men, J Hypertens, 15(12 Pt1), 1449, 1997.

[55]Barker, D.J.P., Osmond, C., Golding, J., et al., Growth in utero, blood pressure in childhood and adult life, and mortality from cardiovascular disease, Br Med J, 298, 564, 1989.

[56]Law, C.M. and Shiell, A.W., Is blood pressure inversely related to birthweight? The strength of evidence from a systematic review of the literature, J Hypertens, 14, 935, 1996.

[57]Curhan, G.C., Willet, W., Rimm, E.B., et al., Birth weight and adult hypertension and diabetes mellitus in U.S. men, Am J Hypertens, 9(4 Suppl 1), 11A, 1996.

[58]Poulter, N., Chang, C.L., McGregor, A., et al., Differences in birth weight between twins and subsequent differences in blood pressure levels, J Human Hypertens, 12(11), 792, 1998.

[59]Chapman, N., Dell’omo, G., Sartini, M.S., et al., Peripheral vascular disease is associated with abnormal diameter relationships at bifurcations in the human retina, Clinical Science, 103, 111, 2002.

[60]The ARIC Investigators, The Atherosclerosis Risk in Communities (ARIC) study: design and objectives, Am J Epidemiol, 129, 687, 1989.

[61]Klein, R., Klein, B.E.K., Linton, K.L.P., et al., The Beaver Dam Eye Study: visual acuity, Ophthalmology, 98, 1310, 1991.

[62]Wong, T.Y., Klein, R., Sharrett, A.R., et al., Retinal arteriolar diameter and risk for hypertension, Ann Intern Med, 140, 248, 2004.

[63]Wong, T.Y., Shankar, A., Klein, R., et al., Prospective cohort study of retinal vessel diameters and risk of hypertension, Br Med J, 329, 79, 2004.

[64]Wong, T.Y., Klein, R., Couper, D.J., et al., Retinal microvascular abnormalities and incident stroke: the Atherosclerosis Risk in Communities Study, Lancet, 358(9288), 1134, 2001.

[65]Wong, T.Y., Klein, R., Sharrett, A.R., et al., Retinal arteriolar narrowing and risk of coronary heart diseases in men and women: the Atherosclerosis Risk in Communities Study, JAMA, 287, 1153, 2002.

[66]Wong, T.Y., Klein, R., Sharrett, A.R., et al., Retinal arteriolar narrowing and risk of diabetes mellitus in middle-aged persons, JAMA, 287, 2528, 2002.

[67]Wong, T.Y., Klein, R., Nieto, F.J., et al., Retinal microvascular abnormalities and 10-year cardiovascular mortality: a population based case-control study, Ophthalmology, 110, 933, 2003.

[68]Wong, T.Y., Knudtson, M.D., Klein, R., et al., A prospective cohort study of retinal arteriolar narrowing and mortality, Am J Epidemiol, 159, 819, 2004.

[69]Michelson, E.L., Morganroth, J., Nichols, C.W., et al., Retinal arteriolar changes as an indicator of coronary artery disease, Arch Intern Med, 139, 1139, 1979.

[70]Gelman, R., Martinez-Perez, M.E., Vanderveen, D.K., et al., Diagnosis of Plus disease in retinopathy of prematurity using retinal image multi-scale analysis,

Invest Ophthalmol Vis Sci, 46, 4734, 2005.

[71]Hughes, A.D., Martinez-Perez, M.E., Jabbar, A.S., et al., Quantification of topological changes in retinal vascular architecture in essential and malignant hypertension, J Hypertens, 24(5), 889, 2006.

[72]Ezquerra, N., Capell, S., Klein, L., et al., Model-guided labeling of coronary structure, IEEE Trans Med Imag, 17, 429, 1998.

[73]Harris, K., Efstratiadis, S.N., Maglaveras, N., et al., Model-based morphological segmentation and labeling of coronary angiograms, IEEE Trans Med Imag, 18, 1003, 1999.

[74]Williams, J. and Wolff, L., Analysis of the pulmonary vascular tree using differential geometry-based vector fields, Comp Vis Image Und, 65, 226, 1997.

[75]Sauret, V., Goatman, K.A., Fleming, J.S., et al., Semi-automatic tabulation of the 3D topology and morphology of branching networks using CT: application to the airway tree, Phys Med Biol, 44, 1625, 1999.

[76]MacDonald, N., Trees and Networks in Biological Models, John Wiley & Sons, New York, 1983.

[77]Horsfield, K., Relea, F.G., and Cumming, G., Diameter, length and branching ratios in the bronchial tree, Resp Physiol, 26, 351, 1976.

[78]Phillips, C.G., Kaye, S.R., and Schroter, R.C., Diameter-based reconstruction of the branching pattern of the human bronchial tree. Part I. Description and application, Resp Physiol, 98, 193, 1994.

[79]Huang, W., Yen, R.T., McLaurine, M., et al., Morphometry of the human pulmonary vasculature, J Appl Physiol, 81(5), 2123, 1996.

[80]Dawson, C.A., Krenz, G.S., Karau, K.L., et al., Structure-function relationships in the pulmonary arterial tree, J Appl Physiol, 86(2), 569, 1999.

[81]Schreiner, W., Neumann, F., Neumann, M., et al., Anatomical variability and funtional ability of vascular trees modeled by constrained constructive optimization, J Theor Biol, 187, 147, 1997.

[82]Schroder,¨ S., Brab, M., Schmid-Schonbein,¨ G.W., et al., Microvascular network topology of the human retinal vessels, Fortschr Ophthalmol, 87, 52, 1990.

[83]Berntson, G.M., The characterization of topology: a comparison of four topological indices for rooted binary trees, J Theor Biol, 177, 271, 1995.

[84]Akita, K. and Kuga, H., A computer method of understanding ocular fundus images, Pattern Recogn, 15, 431, 1982.

[85]Wu, D.C., Schwartz, B., Schwoerer, J., et al., Retinal blood vessel width measured on color fundus photographs by image analysis, Acta Ophthalmol Scand, Suppl 215, 33, 1995.

[86]Zhou, L., Rzeszotarski, M.S., Singerman, L.J., et al., The detection and quantification of retinopathy using digital angiograms, IEEE Trans Med Imag, 13, 619, 1994.

[87]Tolias, Y.A. and Panas, S.M., A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering, IEEE Trans Med Imag, 17, 263, 1998.

[88]Gao, X., Bharath, A., Stanton, A., et al., Quantification and characterisation of arteries in retinal images, Comput Meth Prog Biomed, 63(2), 133, 2000.

[89]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, 2003.

[90]Chaudhuri, S., Chatterjee, S., Katz, N., et al., Detection of blood vessels in retinal images using two-dimensional matched filters, IEEE Trans Med Imag, 8, 263, 1989.

[91]Zana, F. and Klein, J.C., Robust segmentation of vessels from retinal angiography, in Proc 13th Int Conf Digital Signal Processing, Santorini, Greece, 1997, vol. 2, 1087–90.

[92]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 Imag, 19, 203, 2000.

[93]Sinthanayothin, C., Boyce, J.F., Cook, H.L., et al., Automated localisation of the optic disc, fovea and retinal blood vessels from digital colour fundus images, Br J Ophthalmol, 83, 902, 1999.

[94]Staal, J., Abramoff,` M.D., Niemeijer, M., et al., Ridge-based vessel segmentation in color images of the retina, IEEE Trans Med Imag, 23, 501, 2004.

[95]Koenderink, J.J., The structure of images, Biol Cybern, 50, 363, 1984.

[96]Koller, T.M., Gerig, G., Szekely,´ G., et al., Multiscale detection of curvilinear structures in 2D and 3D image data, in Fifth International Conference on Computer Vision, 1995, 864–69.

[97]Lorenz, C., Carlsen, I.C., Buzug, T.M., et al., Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images, in Proc CVRMed-MRCAS’97, J. Troccaz, E. Grimson,

and R. Mosges,¨ eds., 1997, vol. 1205 of Lecture Notes in Computer Science, 233–242.

[98]Sato, Y., Nakajima, S., Atsumi, H., et al., 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images, in CVRMed-MRCAS’97, J. Troccaz, E. Grimson, and R. Mosges,¨ eds., 1997, vol. 1205 of Lecture Notes in Computer Science, 213–222.

[99]Frangi, A.F., Niessen, J.W., Vincken, K.L., et al., Multiscale vessel enhancement filtering., in Medical Image Computing and Computer-Assisted Intervention MICCAI’98, W.M. Wells, A. Colchester, and D. S., eds., Springer, 1998, vol. 1496 of Lecture Notes in Computer Science, 130–7.

[100]Krissian, K., Malandain, G., Ayache, N., et al., Model-based detection of tubular structures in 3D images, Computer Vision and Image Understanding, 80(2), 130, 2000.

[101]Aylward, S.R. and Bullitt, E., Initialization, noise, singularities, and scale in height ridge traversal for tubular objects centerline extraction, IEEE Trans Med Imag, 21(2), 61, 2002.

[102]Jomier, J., Wallace, D.K., and Aylward, S.R., Quantification of retinopathy of prematurity via vessel segmentation, in Medical Image Computing and Computer-Assisted Intervention (MICCAI’03), 2003, vol. 2879 of Lecture Notes in Computer Science, 620–626.

[103]Sofka, M. and Stewart, C.V., Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures, IEEE Trans Med Imag, 25(12), 1531, 2006.

[104]Martinez-Perez, M.E., Hughes, A.D., Stanton, A.V., et al., Retinal blood vessel segmentation by means of scale-space analysis and region growing, in Medical Image Computing and Computer-Assisted Intervention (MICCAI’99), 1999, vol. 1679 of Lecture Notes in Computer Science, 90–97.

[105]Martinez-Perez, M.E., Hughes, A.D., Thom, S.A., et al., Segmentation of blood vessels from red-free and fluorescein retinal images, Med Image Anal, 11(1), 47, 2007.

[106]Martinez-Perez, M.E., Hughes, A.D., Stanton, A.V., et al., Retinal vascular tree morphology: a semi-automatic quantification, IEEE Trans Biomed Eng, 49(8), 912, 2002.

[107]Witkin, A.P., Scale-space filtering: a new apporach to multi-scale description, in Image Understanding, S. Ullman and W. Richards, eds., Ablex Publishing Co., Norwood, NJ, 79–95, 1984.

[108]Eberly, D., Ridges in Image and Data Analysis, Computational Imaging and Vision, Kluwer Academic, Netherlands, 1996.

[109]Lindeberg, T., On scale selection for differential operators, in Proc 8th Scan-

dinavian Conference on Image Analysis, K. Heia, K.A. Hogdra, and B. B., eds., Tromso, Norway, 1993, vol. 2, 857–866.

[110]Stolk, R.P., Vingerling, J.R., Cruickshank, J.K., et al., Rationale and design of the AdRem study: evaluating the effects of blood pressure lowering and intensive glucose control on vascular retinal disorders in patients with type 2 diabetes mellitus, Contemp Clin Trials, 28, 6, 2007.

management. Telemedicine means exchange of clinical data via communication networks to improve patient care. Application of telemedicine in ophthalmology and teleophthalmology has already become a common tool in mainstream eye care delivery in many countries [1]. Telemedicine service delivery offers a number of significant advantages over conventional face-to-face consultations. Major benefits of telemedicine are: (1) service access can be improved considerably in remote areas,

(2)telemedicine is cost effective due to reduction in patient or staff transfers [2],

(3)patients have the benefit of receiving treatment where they live without travelling, and (4) training of local medical officers and nurses to gain knowledge about eye diseases and diagnosis.

There are two modes of delivery in telemedicine. One is real-time video conferencing, which is widely used for emergency and psychiatry consultations. Real-time consultations require high-bandwidth and scheduling. They need both clinician and patient to be present at the same time. This may be not possible in the case of specialists like ophthalmologists. In ophthalmology, high-resolution color images are required to capture the fine detail in the eye. Image files can be large and transmission of these high-resolution images can be time intense. Therefore, teleophthalmology is normally not suitable for video conferencing.

The second mode is called store-and-forward, where images are captured, compressed, and stored in the computer for transmission at a later stage. This is a costeffective mode that utilizes low bandwidth (Internet) and can send high resolution images. Ophthalmology is an image-based diagnostic field, and most of the diseases can be identified from retinal and anterior segment images. Therefore, specialist ophthalmic care can be easily delivered using the store-and-forward method.

11.2.1Image capture

One of the procedures of the store-and-forward method is image capture. It is the most important part of the system. There are three different imaging devices, namely the slitlamp, fundus camera, and ophthalmoscope, to image the anterior segment and retina. Corneal opacification from trachomatous scarring, vitamin A deficiency, injuries, or bacterial and viral keratitis can be readily imaged using a video slitlamp biomicroscope or external macro-photography. Cataract can also be documented by each of these methods, or by the use of more complicated photographic systems. Glaucomatous cupping of the optic disc can be detected by standard ophthalmoscopy, fundus photography, and stereo fundus photographic systems or by scanning laser ophthalmoscopy [3; 4]. Diabetic retinopathy is detected by slitlamp biomicroscopy with a fundus lens (+78 to +90D), fundus photography (large or small pupil instruments), or by conventional ophthalmoscopy. The relative efficacy of these methods has been examined in several field trials [5; 6]. Each examination method has advantages and disadvantages related to portability, cost of equipment, ability to obtain hard copy or digitized records, resolution of images, and ease of use. There are other, more advanced but also very expensive imaging devices such as OCT (Optical Coherent Tomography) and SLO (Scanning Laser Ophthalmoscope) to study the retina. These devices are limited to major eye clinics.

11.2.2Image resolution

Image resolution, which impacts on image quality, is very important for the diagnosis of eye conditions. Poor quality images may lead to poor diagnosis. The resolution of any optical instrument is defined as the shortest distance between two points on a specimen that can still be distinguished as separate entities, first by the optical camera system and second by the observer. The pixelated camera sensor samples the continuous optical images of the retina that the human ocular system and optical components form on the surface of the sensor. For both, the optical image formation and the periodic sampling at discrete points in time and space, there are well established theories that describe the dependence of the achievable resolution on myriads of imaging conditions and parameters (such as wavelength, illumination or imaging cone, spatial or temporal cut-off frequencies, signal-to-noise ratio, and signal strengths).

It is an important design consideration for ophthalmoscopic instruments to match the optical and the digital resolutions. If the digital resolution is too small, then information may be lost. On the other hand, an increase in digital resolution above the optimum value will not improve the definition of the image. However, the disadvantages are: (1) a sensor with a larger pixel number is more expensive; (2) the light sensed per pixel is less, which gives rise to more noise in the image; and (3) the image file size increases, making it more time consuming to process and transmit the file.

Due to considerations for eye safety (maximum permissible exposure) and patient comfort the power of the illuminating light is restricted to certain upper limits, depending on wavelengths and exposure time. As a consequence, the information being reflected off the retina containing light is very weak. Or in other words, the number of signal photons arriving at the sensor for detection is small. This, together with the inherent shot noise (Poisson statistics) of light bursts, limits the number of distinguishable grey levels in digital ophthalmic instruments. A dynamic range of 8-bit in each color band is therefore more than sufficient for the presentation of such images to ophthalmologists or for quantitative data analyses.

In Table 11.1, the required digital image resolution (pixel number) of the camera is given in terms of the desired retinal feature size to be resolved (the smallest value of 3.5 mm results from the maximum obtainable optical resolution with normal human