視網膜影像廣泛地被使用在各種疾病的臨床診斷和治療上，透過對糖尿病患視網膜影像中血管圖像的觀察及偵測出可能的微血管變化，是一個重要的病變研判方式。在視網膜影像內血管進行研判和量度能有效完成對臨床病兆特徵進行定量評估，並可針對糖尿病患在視網膜病變方面的治療進行早期診斷和有效監控。本論文提出一套視神經盤與視網膜血管定位系統，利用簡單的最大投影量及數位影像處理的方法來完成定位任務。這套系統分為兩部份，分別為視神經盤定位與視網膜血管萃取模組。視神經盤定位模組先利用最大投影量的方法切割出一個包含完整視神經盤影像，減少背景雜訊影響定位誤差，再利用最小H轉換（H-minima transform）提高視神經盤與背景的對比，最後再利用最大投影量計算出視神經盤的圓心與半徑。在視網膜血管萃取模組中，先用多尺度技術，萃取出血管的梯度變化量與主要曲率這兩個血管特徵。利用這兩者特徵對原始影像血管部位做強化，提高血管與眼球組織之間的對比，再利用二值化方法定位出視網膜血管位置。在血管定位部份，本論文由台中榮總醫院資料庫（TCVGH）與公開資料庫DRIVE中收集了67張影像來測試所提出的方法。實驗結果顯示，在DRIVE資料庫中，定位結果達到84.12%正確接受率（true positive rate, TPR），3.82%錯誤接受率（false positive rate, FPR）,與81.9%準確性（accuracy, AC）。在榮總資料庫中，定位結果達到85.61%正確接受率，3.17%錯誤接受率,與82.8%準確性。最後本論文針對實驗的數據結果進行分析比較，來驗證建構本系統時所提的相關推論，以供後續研究做為參考。

The retinal images are widely used in diagnosis and treatment of various diseases in clinics. An important aspect of diabetic retinopathy (DR) is the micro-vascular changes that cause detectable changes in the appearance of retinal blood vessels. Identification and measurement of blood vessels in retinal images could allow quantitative evaluation of clinical features, which may allow early diagnosis and effective monitoring of therapies in retinopathy. This thesis presents a retina localization system of optic disk (OD) and vessels. We will use simple techniques which are maximum project and digital image processing to complete this task. The propose system consists of two modules: OD location module and vessel extraction module. In OD location module, we segment complete OD image by using maximum projection in order to reduce locating error suffering from background noise. Then, we use H-minima transform to enhance the contrast between OD and background. Finally, we find the center and radius of OD by using maximum projection again. In vessel extraction module, we use multi-scale technique to extract two vessel features, which are the gradient magnitude and principal curvature of retinal vessel. Using these two vessel features can enhance the contrast between retinal vessel and background. Finally, we locate retina by binarization. In this thesis, 67 images are collected from Taichung Veterans General Hospital (TCVGH) database and a public database, named DRIVE. In DRIVE database, the experimental results show true positive rate (TPR) of 84.12%, false positive rate (FPR) of 3.82%, and accuracy (AC) of 81.9%. In TCVGH database, the experimental results show TPR of 85.61%, FPR of 3.17%, and AC of 82.8%. This thesis also analyzes the experimental results and provides useful information for future research.

誌謝 i
論文摘要 ii
Abstract iii
目錄 iv
1.1 研究動機與目的 1
1.2 研究背景 3
1.3 論文大綱與組織 5
第二章 論文回顧 6
2.1 視神經盤定位 6
2.2 視網膜影像血管萃取 7
2.2.1 匹配濾波器 7
2.2.2 多尺度技術與區域成長趨近 8
2.2.3 形態學與曲率估測法 8
2.2.4 以辨識為基礎局部臨界值分類法 9
2.2.5 像素分類法 9
第三章 研究方法 10
3.1 系統架構 10
3.2 視神經盤定位 12
3.2.1 視神經盤定位流程 12
3.2.2 感興趣區域切割 13
3.2.3 影像強化 17
3.2.4 視神經盤定位 21
3.3 視網膜血管萃取 22
3.3.1 視網膜血管萃取流程 23
3.3.2 視網膜血管邊緣萃取 24
3.3.3 視網膜血管特徵強化 30
第四章 實驗結果與討論 35
4.1 視神經盤定位結果 36
4.2 視網膜血管萃取結果 39
4.3 實驗結果與討論 43
4.3.1 視神經盤定位討論 43
4.3.2 視網膜血管萃取討論 44
第五章 結論與未來研究方向 46
5.1 結論 46
5.2 未來研究方向 47
參考文獻 48

[1]“http://www.tzuchi.com.tw/file/divintro/eye/instr/allegretto/HRA2.htm,” 慈濟眼科.
[2]“http://www.biometricgroup.com/,” Homepage of International Biometric Group.
[3]H. Li and O. Chutatape, “Automatic location of optic disk in retinal images,” in Proc. IEEE-ICIP, vol. 2, pp. 837-840, Oct. 2001.
[4]H. Li and O. Chutatape, “Automated feature extraction in color retinal images by a model based approach,” IEEE Trans. on Biomedical Engineering, vol. 51, No. 2, pp. 246-254, Feb. 2004.
[5]J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder and E. Fletcher, “Optic nerve head segmentation,” IEEE Trans. on Medical Imaging, vol. 23, No. 2, pp. 256-264, Feb. 2004.
[6]A.Osareh, M.Mirmehdi, B.Tomas and R.Markham, “Comparison of colour spaces for optic disc localisation in retinal images,” IEEE 16th International Conference on pattern Recognition, vol. 1, pp. 743-746, 2002.
[7]J. Xu, O. Chutatape and P. Chew, “Automated detection of optic disk boundary by a new deformable model technique,” Proceedings of 27th IEEE Conference on Engineering in Medicine and Biology, Shanghai, China, September 1-4, 2005.
[8]J. Xu, O. Chutatape and P. Chew, “Automated optic disk boundary detection by modified active contour model,” IEEE Trans. on Biomedical Engineering, vol. 54, No. 3, pp. 473-482, Mar. 2007.
[9]M. Lalonde, M. Beaulieu and L. Gagnon, “Fast and robust optic disk detection using pyramidal decomposition and Hausdorff-based template matching,” IEEE Trans. on Medical Imaging, vol. 20, No. 11, pp. 1193-1200, Nov. 2001.
[10]T. Walter, J. C. Klein, P. Massin and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy—Detection of exudates in color fundus images of the human retina,” IEEE Trans. on Medical Imaging, vol. 21, No. 10, pp. 1236-1243, Oct. 2002.
[11]D. Koozekanani, K. Boyer, C. Roberts and S. Katz, “Tracking the optic nerve in OCT video using dual eigenspaces and an adaptive vascular distribution model,” Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, vol. I, pp. I934-I941, Dec. 2003.
[12]S. Tamura, Y. Okamoto and K. Yanasha, “Zerocrossing interval correction in tracking eye-fundus blood vessels,” Pattern Recognition, vol. 21, No. 3, pp. 227-233, 1988.
[13]A. Hoover and M. Goldbaum, “Fuzzy convergence,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 716-721, 1998.
[14]A. Hoover and Goldbaum M., “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,” IEEE Trans. on Medical Imaging, vol. 22, No. 8, pp. 951-958, Aug. 2003.
[15]Y.-P. Chu, S.-F. Yang-Mao, Y.-F. Chen, Y.-K. Chan, C.-S. Tsai and W.-C. Chiang, “Optic disk locating of the retinal fundus images using simple statistic parameters,” Proceedings of the 3rd IASTED International Conference on Biomedical Engineering, pp.86-89, 2005.
[16]S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. on Medical Imaging, vol. 8, No. 3, pp. 263-269, 1989.
[17]M. E. Martinez-Perez, A. D. Hughes, S. A. Thom, A. A. Bharath, and K. H. Parker, “Segmentation of blood vessels from red-free and fluorescein retinal images,” Medical Image Analysis, vol. 11, No. 1, pp. 47-61, 2007.
[18]F. Zana and J. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. on Image Processing, vol. 10, No. 7, pp. 1010-1019, 2001.
[19]X. Jiang and D. Mojon, “Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, No. 1, pp. 131-137, 2003.
[20]M. Niemeijer, J. Staal, B. van Ginneken, M. Loog and M. Abr`amoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” SPIE Medical Imaging, vol. 5370, pp. 648-656, 2004.
[21]R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd Edition, Prentice-Hall, New Jersey, 2002.
[22]P. Soille, Morphological Image Analysis: Principles and Applications, Springer-Verlag, pp. 170-171. 1999.