肺動脈高血壓症是一種心肺血液流動異常且嚴重的疾病，末期甚至引發心臟衰竭。醫學影像(例如：心臟超音波影像、磁共振造影與電腦斷層掃描)具有非侵入式的特性，近幾年來被廣泛地應用。其中電腦斷層血管造影量測所得右肺動脈管壁收縮擴張性被指出對於肺動脈高血壓症是一個可靠的診斷參考。
本研究提出了一個準確地自動化分割電腦斷層血管造影上動脈的方法，演算法可分為兩個步驟：一、產生初始輪廓與二、邊緣修正。一、對一連串不同心跳相位上的原始影像二值化以遮罩出適當的血管灰階範圍，其次將二值影像執行一系列的形態學影像處理並配合兩道簡單的手動處理，以此決定初始輪廓。無血管碰觸的初始輪廓即可當作自動分割的最後結果，其餘必須進一步修正邊緣。二、利用一個橢圓擬合的方法自動定位血管中心點並且執行光線投射演算法尋找可能的血管邊界。將光線所捕捉到的游離線段連接，即可完成血管的自動分割。再者，由結果可量測出不同心跳相位的動脈截面積並且獲得收縮擴張性數值。本研究計算出病患與正常人的四個動脈之管壁收縮擴張性，這些動脈包含了主動脈、主肺動脈與左右肺動脈。除此以外，五位受試對象具有手動圈選的結果，可將自動圈選的結果與之比較以評估本方法的可靠程度。

Pulmonary artery hypertension (PAH), which is diagnosed by an abnormal increase of blood pressure in the pulmonary artery, can be a severe disease, leading to heart failure. In recent years, medical imaging, such as echocardiography, magnetic resonance imaging (MRI), and computed tomography (CT), has been widely used due to its non-invasive property. Right pulmonary artery (RPA) wall distensibility derived from CT angiography was reported to serve as a reliabile marker for the diagnosis of PAH.
This study presented a robust method for automatic segmentation of artery based on CT angiography. The algorithm can be divided into two steps: generation of initial contour and refinement of edge. In the first step, a series of original images at different cardiac phases were thresholded to retrieve appropriate intensity window of vessels, followed by the determination of initial contours by a series of morphological image processing on the binary images with two simple manual initializations. Initial contours without touching can be taken as the final results of segmentation, when others need further refinement of edge. In the second step, the center of vessel was automatically located by an ellipse fitting method and then the ray casting algorithm was applied to search for possible edge. Disconnected segments of edge will be linked to complete the vessel segmentation. Furthermore, cross-sectional areas of arteries at different cardiac phases can be measured and used to obtain distensibility. In this study, artery wall distensibility of patients and healthy subjects was evaluated on four vessels, including aorta, main pulmonary artery, right and left pulmonary artery. In addition, segmentation results of five subjects were compared with those obtained by manual selection to evaluate the reliability of the proposed method.

論文審定書............................................................................. ii
摘要 ........................................................................................iii
Abstract................................................................................. iv
目 錄 ....................................................................................... v
圖目錄 ................................................................................... vi
表目錄 ................................................................................. viii
第一章 簡介 .......................................................................... 1
1.1 肺動脈高血壓症的診斷 ................................................. 1
1.2 肺動脈影像分割 ............................................................. 3
1.3 研究目的 ......................................................................... 5
第二章 自動分割實現方法 .................................................. 6
2.1 產生初始輪廓 ................................................................. 7
2.1.1 原始影像灰階上的處理 ............................................. 7
2.1.2 製作切割線 ............................................................... 12
2.1.3 使用者的手動處理 ................................................... 20
2.1.4 判斷是否為最後結果 ............................................... 26
2.2 邊緣修正 ...................................................................... 31
2.2.1 尋找血管中心點 ....................................................... 31
2.2.2 光線投射演算法 ....................................................... 35
2.2.3 連接線段 ................................................................... 42
2.3 程式執行過程 .............................................................. 44
第三章 結果與比較 ............................................................ 50
3.1 影像取得與實現平台 .................................................. 50
3.2 自動分割結果 .............................................................. 53
3.2.1 輪廓與面積變化 ....................................................... 54
3.2.2 收縮擴張性的展示 ................................................... 60
3.3 與手動圈選比較 .......................................................... 63
第四章 討論與方法取捨 .................................................... 74
4.1 像素大小問題與取捨 .................................................. 74
4.1.1 邊緣問題 ................................................................... 74
4.1.2 雜訊問題 ................................................................... 77
4.2 邊緣檢測的標準差與雜訊 .......................................... 79
4.3 結論 .............................................................................. 82
參考文獻 ............................................................................. 85

[1] N. Galie, et al., "Guidelines for the diagnosis and treatment of pulmonary hypertension: The Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS), endorsed by the International Society of Heart and Lung Transplantation (ISHLT)," European Heart Journal, vol. 30, pp. 2493-2537, 2009.
[2] C. P. Denton, et al., "Comparison of Doppler echocardiography and right heart catheterization to assess pulmonary hypertension in systemic sclerosis," Rheumatology, vol. 36, pp. 239-243, February 1, 1997.
[3] E. Laffon, et al., "A Computed Method for Noninvasive MRI Assessment of Pulmonary Arterial Hypertension," Journal of Applied Physiology, vol. 96, pp. 463-468, February 1, 2004.
[4] A. Devaraj, et al., "Detection of Pulmonary Hypertension with Multidetector CT and Echocardiography Alone and in Combination," Radiology, vol. 254, pp. 609-616, February 1, 2010.
[5] M.-P. Revel, et al., "Pulmonary Hypertension: ECG-gated 64-Section CT Angiographic Evaluation of New Functional Parameters as Diagnostic Criteria," Radiology, vol. 250, pp. 558-566, February 2009.
[6] P. P. Bruyant, et al., "Streak Artifact Reduction in Filtered Backprojection Using a Level Line–Based Interpolation Method," Journal of Nuclear Medicine, vol. 41, pp. 1913-1919, November 1, 2000.
[7] I. A. Cunningham and R. Shaw, "Signal-to-Noise Optimization of Medical Imaging Systems," J. Opt. Soc. Am. A, vol. 16, pp. 621-632, 1999.
[8] Z. Jiehui, et al., "Segmentation of Pulmonary Artery Based on CT Angiography Image," in Pattern Recognition (CCPR), 2010 Chinese Conference on, 2010, pp. 1-5.
[9] R. e. Sebbe, et al., "Pulmonary Arteries Segmentation and Feature Extraction through Slice Marching," in Proc.RISC workshop on Circuits, Systems and Signal Processing, 2003.
[10] M. G. Linguraru, et al., "Segmentation and Quantification of Pulmonary Artery for Noninvasive CT Assessment of Sickle Cell Secondary Pulmonary Hypertension," Medical Physics, vol. 37, p. 1522, 2010.
[11] T. Kitasaka, et al., "Automated Extraction of Aorta and Pulmonary Artery in Mediastinum from 3D Chest X-Ray CT Images without Contrast Medium," in Proc. SPIE, 2002.
[12] Y. Masutani, et al., "Computerized Detection of Pulmonary Embolism in Spiral CT Angiography based on Volumetric Image Analysis," Medical Imaging, IEEE Transactions on, vol. 21, pp. 1517-1523, 2002.
[13] H. Bouma, et al., "Automatic Detection of Pulmonary Embolism in CTA Images," Medical Imaging, IEEE Transactions on, vol. 28, pp. 1223-1230, 2009.
[14] R. C. Gonzalez and R. E. Woods, "Digital Image Processing," 3rd ed: Pearson/Prentice Hall, 2009, pp. 649-710.
[15] O. Wink, et al., "Fast Delineation and Visualization of Vessels in 3-D Angiographic Images," Medical Imaging, IEEE Transactions on, vol. 19, pp. 337-346, 2000.
[16] G. N. Hounsfield, "Computerized Transverse Axial Scanning (Tomography): Part 1. Description of System," British Journal of Radiology, vol. 46, pp. 1016-1022, December 1, 1973.
[17] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," Systems, Man and Cybernetics, IEEE Transactions on, vol. 9, pp. 62-66, 1979.
[18] T. Pavlidis and Y. T. Liow, "Integrating Region Growing and Edge Detection," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 12, pp. 225-233, 1990.
[19] A. Fitzgibbon, et al., "Direct Least Square Fitting of Ellipses," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 21, pp. 476-480, 1999.
[20] J. Canny, "A Computational Approach to Edge Detection," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. PAMI-8, pp. 679-698, 1986.
[21] R. W. Frick, "The Appropriate Use of Null Hypothesis Testing," Psychological Methods, vol. 1, pp. 379-390, 1996.
[22] J. J. Bartko, "The Intraclass Correlation Coefficient as A Measure of Reliability," Psychological Reports, vol. 19, pp. 3-11, 1966/08/01 1966.