全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

A Method for Visualization of Fine Retinal Vascular Pulsation Using Nonmydriatic Fundus Camera Synchronized with Electrocardiogram

DOI: 10.1155/2013/865834

Full-Text   Cite this paper   Add to My Lib

Abstract:

Pulsatile changes in retinal vascular geometry over the cardiac cycle have clinical implication for diagnosis of ocular and systemic vascular diseases. In this study, we report a Vesselness Mapping of Retinal Image Sequence (VMRS) methodology to visualize the vessel pulsation and quantify the pulsatile motions in the cardiac cycle. Retinal images were recorded in an image sequence corresponding to 8 segments of the cardiac cycle using a nonmydriatic fundus camera (Canon CR45, Canon Inc., Japan) modified with ECG-synchronization. Individual cross-sectional vessel diameters were measured separately and the significance of the variations was tested statistically by repeated measures analysis of variance (ANOVA). The graders observed an improved quality of vessel pulsation on a wide region around the optic disk using the VMRS. Individual cross- sectional vessel diameter measurement after visualization of pulsatile motions resulted in the detection of more significant diameter change for both arterioles (3.3? m, ) and venules (6.6? m, ) compared to individual measurement without visualization of the pulsatile motions (all P values >?0.05), showing an increase of 2.1? m and 4.7? m for arterioles and venules, respectively. 1. Introduction Retinal imaging has enabled direct and in vivo assessment of human’s body circulation system and is applied for the detection of major systemic vascular diseases, including ischemia [1], coronary heart diseases [2] and diabetes mellitus [3] and its complications [4–6]. A number of studies [7, 8] have also reported the clinical application of dynamic retinal image processing for the investigation of pulsatile properties influenced by cardiac rhythm over time. This pulsatility is expected as a result of change in blood volumetric flow entering the ophthalmic vascular system under certain level of intraocular pressure during the peak systolic and diastolic phases of cardiac cycle, which can serve as a potential feature to rule out some clinical signs. An example of pulsatile property observable from the retina is the spontaneous venous pulsation (SVP), which is available in approximately 90% of the patients [9, 10]. It is caused by the variation in the pressure gradient between the intraocular retinal veins and the retrolaminar portion of the central retinal vein (CRV) [11], visible as rhythmic changes the in diameter of one or more veins near or on the optic nerve head. Its clinical relevance is for differentiating early papilledema from pseudopapilledema, detection of elevated intracranial pressure (≥14?mmHg), and other

References

[1]  N. Witt, T. Y. Wong, A. D. Hughes et al., “Abnormalities of retinal microvascular structure and risk of mortality from ischemic heart disease and stroke,” Hypertension, vol. 47, no. 5, pp. 975–981, 2006.
[2]  T. Y. Wong, R. Klein, A. R. Sharrett et al., “Retinal arteriolar narrowing and risk of coronary heart disease in men and women: the Atherosclerosis Risk in Communities Study,” JAMA, vol. 287, no. 9, pp. 1153–1159, 2002.
[3]  T. Y. Wong, R. Klein, A. Richey Sharrett et al., “Retinal arteriolar narrowing and risk of diabetes mellitus in middle-aged persons,” JAMA, vol. 287, no. 19, pp. 2528–2533, 2002.
[4]  M. B. Sasongko, J. J. Wang, K. C. Donaghue et al., “Alterations in retinal microvascular geometry in young type 1 diabetes,” Diabetes Care, vol. 33, no. 6, pp. 1331–1336, 2010.
[5]  M. B. Sasongko, T. Y. Wong, K. C. Donaghue et al., “Retinal arteriolar tortuosity is associated with retinopathy and early kidney dysfunction in type 1 diabetes,” American Journal of Ophthalmology, vol. 153, pp. 176–183, 2012.
[6]  P. Benitez-Aguirre, M. E. Craig, M. B. Sasongko et al., “Retinal vascular geometry predicts incident retinopathy in young people with type 1 diabetes: a prospective cohort study from adolescence,” Diabetes Care, vol. 34, pp. 1622–1627, 2011.
[7]  H. C. Chen, V. Patel, J. Wiek, S. M. Rassam, and E. M. Kohner, “Vessel diameter changes during the cardiac cycle,” Eye, vol. 8, no. 1, pp. 97–103, 1994.
[8]  F. Moret, C. M. Poloschek, W. A. Lagreze, and M. Bach, “Visualization of fundus vessel pulsation using principal component analysis,” Investigative Ophthalmology and Visual Science, vol. 52, pp. 5457–5464, 2011.
[9]  D. N. Levine, “Spontaneous pulsation of the retinal veins,” Microvascular Research, vol. 56, no. 3, pp. 154–165, 1998.
[10]  A. S. Jacks and N. R. Miller, “Spontaneous retinal venous pulsation: aetiology and significance,” Journal of Neurology Neurosurgery and Psychiatry, vol. 74, no. 1, pp. 7–9, 2003.
[11]  G. Lascaratos, S. Ahmed, and S. A. Madill, “Pearls & Oy-sters: spontaneous venous pulsation and its role in differentiating papilledema from pseudopapilledema,” Neurology, vol. 75, no. 13, pp. e53–e54, 2010.
[12]  W. Vilser, E. Nagel, and I. Lanzl, “Retinal Vessel Analysis—new possibilities,” Biomedizinische Technik, vol. 47, pp. 682–685, 2002.
[13]  G. Garhofer, T. Bek, A. G. Boehm et al., “Use of the retinal vessel analyzer in ocular blood flow research,” Acta Ophthalmologica, vol. 88, pp. 717–722, 2010.
[14]  H. Hao, D. K. Kumar, B. Aliahmad, and M. Z. C. Azemin, “Improved retinal photography method and visualization of multiple retinal images,” in Proceedings of the IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE '11), pp. 274–277.
[15]  H. Hao, M. B. Sasongko, T. Y. Wong et al., “Does retinal vascular geometry vary with cardiac cycle?” Investigative Ophthalmology and Visual Science, vol. 53, no. 9, pp. 5799–5805, 2012.
[16]  C. V. Stewart, C. L. Tsai, and B. Roysam, “The dual-bootstrap iterative closest point algorithm with application to retinal image registration,” IEEE Transactions on Medical Imaging, vol. 22, no. 11, pp. 1379–1394, 2003.
[17]  H. Li, W. Hsu, M. L. Lee, and H. Wang, “A piecewise Gaussian model for profiling and differentiating retinal vessels,” in Proceedings of the International Conference on Image Processing (ICIP '03), vol. 1, pp. 1069–1072, September 2003.
[18]  A. H. Sable and K. C. Jondhale, “Modified double bilateral filter for sharpness enhancement and noise removal,” in Proceedings of the International Conference on Advances in Computer Engineering (ACE '10), pp. 295–297, June 2010.
[19]  C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of the IEEE 6th International Conference on Computer Vision, pp. 839–846, January 1998.
[20]  M. Martínez-Pérez, A. Hughes, A. Stanton, S. Thom, A. Bharath, and K. Parker, “Retinal blood vessel segmentation by means of scale-space analysis and region growing,” in Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI '99), C. Taylor and A. Colchester, Eds., vol. 1679 of Lecture Notes in Computer Science, Springer, Berlin, Germany.
[21]  J. V. B. Soares, J. J. G. Leandro, R. M. Cesar, H. F. Jelinek, and M. J. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Transactions on Medical Imaging, vol. 25, no. 9, pp. 1214–1222, 2006.
[22]  M. Vlachos and E. Dermatas, “Multi-scale retinal vessel segmentation using line tracking,” Computerized Medical Imaging and Graphics, vol. 34, no. 3, pp. 213–227, 2010.
[23]  R. C. González and R. E. Woods, Digital Image ProcessIng, Prentice-Hall, Upper Saddle River, NJ, USA, 3rd edition, 2008.
[24]  M. D. Knudtson, K. E. Lee, L. D. Hubbard, T. Y. Wong, R. Klein, and B. E. K. Klein, “Revised formulas for summarizing retinal vessel diameters,” Current Eye Research, vol. 27, no. 3, pp. 143–149, 2003.
[25]  D. K. Kumar, B. Aliahmad, and H. Hao, “Retinal vessel diameter measurement using unsupervised linear discriminant analysis,” ISRN Ophthalmology, vol. 2012, Article ID 151369, 7 pages, 2012.
[26]  B. Aliahmad, D. K. Kumar, S. Janghorban, M. Z. Azemin, H. Hao, and R. Kawasaki, “Automatic retinal vessel profiling using multi-step regression method,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2606–2609, August 2011.
[27]  A. Bhuiyan, R. Kawasaki, E. Lamoureux, T. Y. Wong, and K. Ramamohanarao, “Vessel segmentation from color retinal images with varying contrast and central reflex properties,” in Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA '10), pp. 184–189, December 2010.
[28]  A. Bhuiyan, B. Nath, J. Chua, and R. Kotagiri, “Vessel cross-sectional diameter measurement on color retinal image,” in Biomedical Engineering Systems and Technologies, A. Fred, J. Filipe, and H. Gamboa, Eds., vol. 25, pp. 214–227, Springer, Berlin, Germany, 2009.
[29]  B. Al-Diri, A. Hunter, and D. Steel, “An active contour model for segmenting and measuring retinal vessels,” IEEE Transactions on Medical imaging, vol. 28, no. 9, pp. 1488–1497, 2009.
[30]  H. M. Pakter, S. C. Fuchs, M. K. Maestri et al., “Computer-assisted methods to evaluate retinal vascular caliber: what are they measuring?” Investigative Ophthalmology and Visual Science, vol. 52, no. 2, pp. 810–815, 2011.
[31]  J. B. Jonas, G. C. Gusek, and G. O. H. Naumann, “Optic disc, cup and neuroretinal rim size, configuration and correlations in normal eyes,” Investigative Ophthalmology and Visual Science, vol. 29, no. 7, pp. 1151–1158, 1988.
[32]  K. Gugleta, A. Kochkorov, R. Katamay, C. Zawinka, J. Flammer, and S. Orgul, “On pulse-wave propagation in the ocular circulation,” Investigative Ophthalmology and Visual Science, vol. 47, no. 9, pp. 4019–4025, 2006.
[33]  S. I. Ao, Applied Time Series Analysis and Innovative Computing, Springer, 2010.
[34]  K. E. Kotliar, M. Baumann, W. Vilser, and I. M. Lanzl, “Pulse wave velocity in retinal arteries of healthy volunteers,” British Journal of Ophthalmology, vol. 95, no. 5, pp. 675–679, 2011.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133