請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17392完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞峰 | |
| dc.contributor.author | Yi-Ting Chen | en |
| dc.contributor.author | 陳奕廷 | zh_TW |
| dc.date.accessioned | 2021-06-08T00:10:25Z | - |
| dc.date.copyright | 2013-08-14 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-07 | |
| dc.identifier.citation | [1] K. McGeechan, G. Liew, P. Macaskill et al., “Prediction of incident stroke events based on retinal vessel caliber: a systematic review and individual-participant meta-analysis,” American journal of epidemiology, vol. 170, no. 11, pp. 1323-1332, 2009.
[2] T. Nagaoka, and A. Yoshida, “Relationship between retinal blood flow and renal function in patients with type 2 diabetes and chronic kidney disease,” Diabetes care, vol. 36, no. 4, pp. 957-961, 2013. [3] A. Sommer, J. Katz, H. A. Quigley et al., “Clinically detectable nerve fiber atrophy precedes the onset of glaucomatous field loss,” Archives of ophthalmology, vol. 109, no. 1, pp. 77, 1991. [4] M. K. Ikram, F. J. de Jong, J. R. Vingerling et al., “Are retinal arteriolar or venular diameters associated with markers for cardiovascular disorders? The Rotterdam Study,” Investigative ophthalmology & visual science, vol. 45, no. 7, pp. 2129-2134, 2004. [5] M. K. Ikram, J. A. Janssen, A. M. Roos et al., “Retinal Vessel Diameters and Risk of Impaired Fasting Glucose or Diabetes The Rotterdam Study,” Diabetes, vol. 55, no. 2, pp. 506-510, 2006. [6] F. Skovborg, A. V. Nielsen, E. Lauritzen et al., “Diameters of the retinal vessels in diabetic and normal subjects,” Diabetes, vol. 18, no. 5, pp. 292-298, 1969. [7] T. Y. Wong, M. D. Knudtson, R. Klein et al., “Computer-assisted measurement of retinal vessel diameters in the Beaver Dam Eye Study: methodology, correlation between eyes, and effect of refractive errors,” Ophthalmology, vol. 111, no. 6, pp. 1183-1190, 2004. [8] J. Parr, and G. Spears, “General caliber of the retinal arteries expressed as the equivalent width of the central retinal artery,” Am J Ophthalmol, vol. 77, no. 4, pp. 472-477, 1974. [9] J. Parr, and G. Spears, “Mathematic relationships between the width of a retinal artery and the widths of its branches,” American journal of ophthalmology, vol. 77, no. 4, pp. 478-483, 1974. [10] L. D. Hubbard, R. J. Brothers, W. N. King et al., “Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study,” Ophthalmology, vol. 106, no. 12, pp. 2269-2280, 1999. [11] M. D. Knudtson, K. E. Lee, L. D. Hubbard et al., “Revised formulas for summarizing retinal vessel diameters,” Current eye research, vol. 27, no. 3, pp. 143-149, 2003. [12] M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of machine learning: The MIT Press, 2012. [13] A. B. Tucker, Computer science handbook: CRC press, 2004. [14] J. Hofmann, and M. Sernetz, A kinetic study on the enzymatic hydrolysis of fluorescein diacetate and fluorescein-di-beta-D-galactopyranoside, 1983. [15] A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” Medical Imaging, IEEE Transactions on, vol. 19, no. 3, pp. 203-210, 2000. [16] J. B. Jonas, G. C. Gusek, and G. Naumann, “Optic disc, cup and neuroretinal rim size, configuration and correlations in normal eyes,” Investigative ophthalmology & visual science, vol. 29, no. 7, pp. 1151-1158, 1988. [17] K. D. Pagana, and T. J. Pagana, Mosby's manual of diagnostic and laboratory tests: Elsevier Health Sciences, 2009. [18] J.-M. Geusebroek, A. W. Smeulders, and J. Van De Weijer, “Fast anisotropic gauss filtering,” Image Processing, IEEE Transactions on, vol. 12, no. 8, pp. 938-943, 2003. [19] R. Adams, and L. Bischof, “Seeded region growing,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 16, no. 6, pp. 641-647, 1994. [20] A. Aquino, M. E. Gegundez-Arias, and D. Marin, “Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques,” Medical Imaging, IEEE Transactions on, vol. 29, no. 11, pp. 1860-1869, 2010. [21] V. S. Joshi, M. K. Garvin, J. M. Reinhardt et al., 'Automated artery-venous classification of retinal blood vessels based on structural mapping method.' pp. 83151C-83151C-8. [22] J. Xu, O. Chutatape, and P. Chew, “Automated optic disk boundary detection by modified active contour model,” Biomedical Engineering, IEEE Transactions on, vol. 54, no. 3, pp. 473-482, 2007. [23] J. Matas, O. Chum, M. Urban et al., “Robust wide-baseline stereo from maximally stable extremal regions,” Image and vision computing, vol. 22, no. 10, pp. 761-767, 2004. [24] A. F. Frangi, W. J. Niessen, K. L. Vincken et al., 'Multiscale vessel enhancement filtering,' Medical Image Computing and Computer-Assisted Interventation—MICCAI’98, pp. 130-137: Springer, 1998. [25] T. Zhang, and C. Y. Suen, “A fast parallel algorithm for thinning digital patterns,” Communications of the ACM, vol. 27, no. 3, pp. 236-239, 1984. [26] R. Klein, B. E. Klein, S. E. Moss et al., “Hypertension and retinopathy, arteriolar narrowing, and arteriovenous nicking in a population,” Archives of ophthalmology, vol. 112, no. 1, pp. 92, 1994. [27] R. Klein, B. Klein, Y. L. Magli et al., “An alternative method of grading diabetic retinopathy,” Ophthalmology, vol. 93, no. 9, pp. 1183-1187, 1986. [28] R. A. Haddad, and A. N. Akansu, “A class of fast Gaussian binomial filters for speech and image processing,” Signal Processing, IEEE Transactions on, vol. 39, no. 3, pp. 723-727, 1991. [29] T. M. Apostol, Calculus, Vol. 1: One-Variable Calculus, with an Introduction to Linear Algebra: Wiley New York, 1967. [30] H. Farid, and E. P. Simoncelli, 'Optimally rotation-equivariant directional derivative kernels.' pp. 207-214. [31] H. Samet, and M. Tamminen, “Efficient component labeling of images of arbitrary dimension represented by linear bintrees,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 10, no. 4, pp. 579-586, 1988. [32] L. Tramontan, E. Grisan, and A. Ruggeri, 'An improved system for the automatic estimation of the Arteriolar-to-Venular diameter Ratio (AVR) in retinal images.' pp. 3550-3553. [33] J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms: Kluwer Academic Publishers, 1981. [34] J. Lewis, 'Fast normalized cross-correlation.' pp. 120-123. [35] H. Li, W. Hsu, M.-L. Lee et al., 'A piecewise Gaussian model for profiling and differentiating retinal vessels.' pp. I-1069-72 vol. 1. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17392 | - |
| dc.description.abstract | 視網膜血管是人體唯一可以直接且用非侵入式的方法看到的血管結構，視網膜血管反應系統微血管的狀況像是腦部、心臟和腎臟，之前的研究指出血管的管徑和系統心血管疾病有關聯性。在這篇論文中，提出在視網膜圖片上測量血管管徑和分類動靜脈的自動量化分析，為了得到更準確的血管管徑和分類動靜脈，根據動態螢光眼底影片(dynamic fluorescein angiography)和螢光眼底圖片(fluorescein angiography)的量化測量方式和傳統的彩色眼底圖片(color fundus photography)的方法比較，利用視神經盤和血管的切割的結果來偵測血管，接著，利用顏色強度的特徵分類動靜脈。在動態螢光眼底影片中，在主幹血管上的血管分類偵測率和正確率可以達到100%，相較於傳統的彩色眼底圖片血管分類偵測率97.83%和正確率86.67%，動態螢光眼底影片可以得到較準確的結果。總結來說，在視網膜血管管徑的計算和分類動靜脈上提出量化的方法，使其提供準確的測量結果，對於未來的分析動靜脈管徑的比值和系統心血管疾病的關聯性上有很大的幫助。 | zh_TW |
| dc.description.abstract | Blood vessels of retina are the only vascular structure in the body that can be seen directly and non-invasively. Retinal vessels reflect the conditions of systemic capillaries such as in brain, heart, and kidney. Previous studies have reported the association between vascular diameter and systemic vascular disease. In this study, an automatic quantitative analysis which measured the vascular diameter in retina images and classified the vessels into arteries and veins was proposed. In order to get more accurate vessel width and vessel classification, the measurement based on dynamic fluorescein angiography (DFAG) and fluorescein angiography (FAG) were quantified and were compared with that of the conventional color fundus photography (CFP). Optic disc and vessel segmentation were used to detect the number of vessels. Then, intensity features were extracted for vessel classification. In the DFAG, the detection rate and accuracy of vessel classification achieved 100% for the trunk vessels that were better than those in the CFP (97.83% and 86.67%). Summarily, the proposed quantitative method for retinal vessel width calculation and vessel classification provides an accurate measurement for the future analysis in the correlation between arteriovenous ratio and the systemic vascular diseases. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T00:10:25Z (GMT). No. of bitstreams: 1 ntu-102-R00944003-1.pdf: 2740956 bytes, checksum: 4963b8d3b78f2107a7ec3ca99c414b77 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 口試委員會審定書 I
Acknowledgements II 摘要 III Abstract IV Table of Contents V List of Figures VI List of Tables VIII Chapter 1 Introduction 1 Chapter 2 Material 4 Chapter 3 Method 10 3-1 CFP Analysis 10 3-1-1 Optic Disc Segmentation 11 3-1-2 Vessel Segmentation 17 3-1-3 Vessel Width Calculation 18 3-1-4 Vessel classification 22 3-2 DFAG Analysis 25 3-2-1 Vessel Segmentation 26 3-2-2 CFP and DFAG Registration 27 3-2-3 Vessel classification 28 3-2-4 FAG width calculation 30 Chapter 4 Experimental Results and Discussion 35 4-1 Experimental Results 35 4-2 Discussion 38 Chapter 5 Conclusion and Future Works 41 References 43 | |
| dc.language.iso | en | |
| dc.title | 眼底攝影之動靜脈分析及測量 | zh_TW |
| dc.title | Analysis and Measurement of Artery and Vein in Fundus Photography | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳偉銘,張簡光哲 | |
| dc.subject.keyword | 彩色眼底圖片,螢光眼底圖片,動態螢光眼底影片,血管管徑計算,血管分類, | zh_TW |
| dc.subject.keyword | Color fundus photography,Fluorescein angiography,Dynamic fluorescein angiography,Vessel width calculation,Vessel classification, | en |
| dc.relation.page | 45 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2013-08-07 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-102-1.pdf 未授權公開取用 | 2.68 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。