臺灣博碩士論文加值系統

超音波在診斷與評估頸動脈疾病上是最常使用的，並且在評估和劃分頸動脈斑塊扮演非常重要角色。目前二維超音波影像用電腦輔助分析能夠量測頸動脈窄化程度的資訊，傳統二維超音波頸動脈影像評估窄化程度仍受到維度的限制，二維超音波影像僅提供頸動脈橫切面影像血管窄化的資訊，然而在三維超音波影像中卻能夠準確地偵測頸動脈窄化整體的區域。一般的三維超音波影像可以由連續的二維影像掃描獲得，使用連續二維影像獲得三維影像重建需要準確的二維影像輸入和在三維空間中探頭的精準地位置，在這篇研究當中所提出的三維影像重建演算法是基於動態捕捉技術之手持式超音波系統，演算法包含了頸動脈超音波影像分割技術和使用動態捕捉系統在三維空間中影像的校正。其中影算法分割技術是基於主動輪廓模型方法的改良，其分割演算法的結果分別於頸動脈假體影像和人類頸動脈影像中與手動圈選的輪廓進行比較分析。其結果顯示頸動脈假體影像和人類頸動脈影像的誤差百分比分別為0.8%和4.3%。另一方面影像校正由一般式掃描和抖動式的掃描進行測試，其結果顯示經過校正的過程可明顯降低手持式掃描所造成三維影像失真的問題。

Ultrasonography is commonly used to diagnose and assess carotid artery disease; it plays a crucial role in assessing carotid plaques. Currently, the dimensional information of the carotid artery can be measured using two-dimensional (2D) ultrasound images with computer-assisted analysis. Assessing the dimension of artery stenosis by using conventional 2D images has limitations; it provides only transverse cross-section images of the carotid artery. By contrast, the arterial narrowing region is detected accurately by using three-dimensional (3D) ultrasound imaging. In general, 3D ultrasound images can be acquired using continuous 2D images. Obtaining 3D images of volume reconstruction by using continuous 2D images requires accurate 2D image segmentation and precise probe position data. In this study, an algorithm was proposed to reconstruct an ultrasound image for freehand acquisition based on a motion capture system. This algorithm includes the image segmentation of carotid ultrasound and 3D space image calibration. Image segmentation is based on an active contour model, which is used in the carotid phantom and human carotid, rather than manual tracing. The results indicated that the percentage errors of carotid phantom and human carotid compared with manual tracing were 0.8% and 4.3%, respectively. Conversely, image calibration was tested using normal scanning and tremble scanning, which reduced the freehand, which causes the distortion of 3D reconstruction images.

Contents
摘要 I
Abstract II
致謝 III
Contents IV
Tables VI
Figures VII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Literature Review 3
1.2.1 Image Segmentation 3
1.2.2 Active Contour Model 6
1.2.3 Calibration for Freehand 3D Ultrasound 10
1.3 Motivation and Objectives 14
Chapter 2 Theoretical Basis 15
2.1 Gray-Scale Ultrasonic Imaging 15
2.1.1 Amplitude-Mode Imaging 15
2.1.2 B-Mode Imaging 16
2.2 Image Segmentation 18
2.2.1 Threshold-Based Segmentation Method 18
2.2.2 Region-Based Segmentation Method 19
2.2.3 Edge-Based Segmentation Method 20
2.3 Active Contour Model 22
2.3.1 Traditional Snakes 22
2.3.2 Greedy Snakes 25
2.4 Freehand 3D Ultrasound Calibration 28
Chapter 3 Materials and Methods 31
3.1 Ultrasound System 31
3.2 Optical Tracking System 33
3.2.1. Tracking System Calibration 34
3.3 Image Segmentation Algorithm 37
3.3.1 Initial Contour detection 38
3.3.2 Greedy Snake Algorithm 40
3.4 Image Calibration 43
3.5 Phantom Manufacture 44
3.6 Experimental Procedure 47
Chapter 4 Results and Discussion 48
4.1 Contour Detection 48
4.2 Motion Capture Tracking Calibration 55
4.2.1 Phantom Image Calibration 55
4.3 Reconstruction of 3D images 58
4.3.1 Carotid Phantom with Mechanical Scanning 58
4.3.2 Cylindrical inclusion phantom of calibration 59
4.3.3 Human carotid for freehand scanning 60
Chapter 5 Conclusion 62
Future Work 63
References 64

[1]J. Golledge, R. M. Greenhalgh, and A. H. Davies, “The symptomatic carotid plaque, Stroke, vol. 31, no. 3, pp. 774-781, 2000.
[2]V. L. Roger, A. S. Go, D. M. Lloyd-Jones, R. J. Adams, J. D. Berry, T. M. Brown, M. R. Carnethon, S. Dai, G. de Simone, and E. S. Ford, “Heart disease and stroke statistics—2011 update a report from the American Heart Association, Circulation, vol. 123, no. 4, pp. e18-e209, 2011.
[3]M. L. Bots, A. W. Hoes, P. J. Koudstaal, A. Hofman, and D. E. Grobbee, “Common carotid intima-media thickness and risk of stroke and myocardial infarction the Rotterdam Study, Circulation, vol. 96, no. 5, pp. 1432-1437, 1997.
[4]C. P. Loizou, C. S. Pattichis, M. Pantziaris, T. Tyllis, and A. Nicolaides, “Snakes based segmentation of the common carotid artery intima media, Medical ＆ biological engineering ＆ computing, vol. 45, no. 1, pp. 35-49, 2007.
[5]J. D. Spence, M. Eliasziw, M. DiCicco, D. G. Hackam, R. Galil, and T. Lohmann, “Carotid plaque area a tool for targeting and evaluating vascular preventive therapy, Stroke, vol. 33, no. 12, pp. 2916-2922, 2002.
[6]C. V. Bourantas, I. C. Kourtis, M. E. Plissiti, D. I. Fotiadis, C. S. Katsouras, M. I. Papafaklis, and L. K. Michalis, “A method for 3D reconstruction of coronary arteries using biplane angiography and intravascular ultrasound images, Computerized Medical Imaging and Graphics, vol. 29, no. 8, pp. 597-606, 2005.
[7]S. Zheng, and L. Mengchan, “Reconstruction of coronary vessels from intravascular ultrasound image sequences based on compensation of the in-plane motion, Computerized Medical Imaging and Graphics, vol. 37, no. 7, pp. 618-627, 2013.
[8]M.-A. Janvier, L.-G. Durand, M.-H. R. Cardinal, I. Renaud, B. Chayer, P. Bigras, J. de Guise, G. Soulez, and G. Cloutier, “Performance evaluation of a medical robotic 3D-ultrasound imaging system, Medical image analysis, vol. 12, no. 3, pp. 275-290, 2008.
[9]M.-A. Janvier, G. Soulez, L. Allard, and G. Cloutier, “Validation of 3D reconstructions of a mimicked femoral artery with an ultrasound imaging robotic system, Medical physics, vol. 37, no. 7, pp. 3868-3879, 2010.
[10]M.-A. Janvier, S. Merouche, L. Allard, G. Soulez, and G. Cloutier, “A 3-D Ultrasound Imaging Robotic System to Detect and Quantify Lower Limb Arterial Stenoses: In Vivo Feasibility, Ultrasound in medicine ＆ biology, vol. 40, no. 1, pp. 232-243, 2014.
[11]D. C. Barratt, B. B. Ariff, K. N. Humphries, S. M. G. Thom, and A. D. Hughes, “Reconstruction and quantification of the carotid artery bifurcation from 3-D ultrasound images, Medical Imaging, IEEE Transactions on, vol. 23, no. 5, pp. 567-583, 2004.
[12]M. Egger, J. D. Spence, A. Fenster, and G. Parraga, “Validation of 3D ultrasound vessel wall volume: an imaging phenotype of carotid atherosclerosis, Ultrasound in medicine ＆ biology, vol. 33, no. 6, pp. 905-914, 2007.
[13]B. Chiu, M. Egger, J. D. Spence, G. Parraga, and A. Fenster, “Quantification of carotid vessel wall and plaque thickness change using 3D ultrasound images, Medical physics, vol. 35, no. 8, pp. 3691-3710, 2008.
[14]A. Fenster, G. Parraga, B. Chin, and I. Bax, “Three-dimensional ultrasound imaging, Handbook of 3D Machine Vision: Optical Metrology and Imaging, pp. 285, 2013.
[15]E. Yeom, K.-H. Nam, C. Jin, D.-G. Paeng, and S.-J. Lee, “3D reconstruction of a carotid bifurcation from 2D transversal ultrasound images, Ultrasonics, vol. 54, no. 8, pp. 2184-2192, 2014.
[16]J. A. Noble, and D. Boukerroui, “Ultrasound image segmentation: a survey, Medical Imaging, IEEE Transactions on, vol. 25, no. 8, pp. 987-1010, 2006.
[17]A. R. Abdel-Dayem, and M. R. El-Sakka, Carotid artery ultrasound image segmentation using fuzzy region growing, Image Analysis and Recognition, pp. 869-878:, 2005.
[18]S. Golemati, J. Stoitsis, E. G. Sifakis, T. Balkizas, and K. S. Nikita, “Using the Hough transform to segment ultrasound images of longitudinal and transverse sections of the carotid artery, Ultrasound in medicine ＆ biology, vol. 33, no. 12, pp. 1918-1932, 2007.
[19]J. Stoitsis, S. Golemati, S. Kendros, and K. Nikita, Automated detection of the carotid artery wall in B-mode ultrasound images using active contours initialized by the Hough transform. pp. 3146-3149, 2008.
[20]D. Wang, R. Klatzky, N. Amesur, and G. Stetten, Carotid artery and jugular vein tracking and differentiation using spatiotemporal analysis, Medical Image Computing and Computer-Assisted Intervention–MICCAI 2006, pp. 654-661: Springer, 2006.
[21]D. C. Wang, R. Klatzky, B. Wu, G. Weller, A. R. Sampson, and G. D. Stetten, “Fully automated common carotid artery and internal jugular vein identification and tracking using B-mode ultrasound, Biomedical Engineering, IEEE Transactions on, vol. 56, no. 6, pp. 1691-1699, 2009.
[22]P. Abolmaesumi, M. R. Sirouspour, and S. Salcudean, Real-time extraction of carotid artery contours from ultrasound images. pp. 181-186.
[23]P. Abolmaesumi, S. E. Salcudean, W.-H. Zhu, M. R. Sirouspour, and S. P. DiMaio, “Image-guided control of a robot for medical ultrasound, Robotics and Automation, IEEE Transactions on, vol. 18, no. 1, pp. 11-23, 2002.
[24]J. Guerrero, S. E. Salcudean, J. A. McEwen, B. A. Masri, and S. Nicolaou, “Real-time vessel segmentation and tracking for ultrasound imaging applications, Medical Imaging, IEEE Transactions on, vol. 26, no. 8, pp. 1079-1090, 2007.
[25]F. Mao, J. Gill, D. Downey, and A. Fenster, “Segmentation of carotid artery in ultrasound images: Method development and evaluation technique, Medical physics, vol. 27, no. 8, pp. 1961-1970, 2000.
[26]A. Zahalka, and A. Fenster, “An automated segmentation method for three-dimensional carotid ultrasound images, Physics in medicine and biology, vol. 46, no. 4, pp. 1321, 2001.
[27]E. Ukwatta, J. Awad, A. Ward, D. Buchanan, J. Samarabandu, G. Parraga, and A. Fenster, “Three-dimensional ultrasound of carotid atherosclerosis: semiautomated segmentation using a level set-based method, Medical physics, vol. 38, no. 5, pp. 2479-2493, 2011.
[28] E. Ukwatta, J. Yuan, D. Buchanan, B. Chiu, J. Awad, W. Qiu, G. Parraga, and A. Fenster, “Three-dimensional segmentation of three-dimensional ultrasound carotid atherosclerosis using sparse field level sets, Medical physics, vol. 40, no. 5, pp. 052903, 2013.
[29]A. Fenster, D. B. Downey, and H. N. Cardinal, “Three-dimensional ultrasound imaging, Physics in medicine and biology, vol. 46, no. 5, pp. R67, 2001.
[30]L. Mercier, T. Langø, F. Lindseth, and L. D. Collins, “A review of calibration techniques for freehand 3-D ultrasound systems, Ultrasound in medicine ＆ biology, vol. 31, no. 2, pp. 143-165, 2005.
[31]P. R. Detmer, G. Bashein, T. Hodges, K. W. Beach, E. P. Filer, D. H. Burns, and D. E. Strandness, “3D ultrasonic image feature localization based on magnetic scanhead tracking: in vitro calibration and validation, Ultrasound in medicine ＆ biology, vol. 20, no. 9, pp. 923-936, 1994.
[32]T. C. Hodges, P. R. Detmer, D. H. Burns, K. W. Beach, and D. E. Strandness, “Ultrasonic three-dimensional reconstruction: in vitro and in vivo volume and area measurement, Ultrasound in medicine ＆ biology, vol. 20, no. 8, pp. 719-729, 1994.
[33]R. W. Prager, R. Rohling, A. Gee, and L. Berman, “Rapid calibration for 3-D freehand ultrasound, Ultrasound in medicine ＆ biology, vol. 24, no. 6, pp. 855-869, 1998.
[34]F. Lindseth, G. A. Tangen, T. Langø, and J. Bang, “Probe calibration for freehand 3-D ultrasound, Ultrasound in medicine ＆ biology, vol. 29, no. 11, pp. 1607-1623, 2003.
[35]G. M. Treece, A. H. Gee, R. W. Prager, C. J. Cash, and L. H. Berman, “High-definition freehand 3-D ultrasound, Ultrasound in medicine ＆ biology, vol. 29, no. 4, pp. 529-546, 2003.
[36]D. De Lorenzo, A. Vaccarella, G. Khreis, H. Moennich, G. Ferrigno, and E. De Momi, “Accurate calibration method for 3D freehand ultrasound probe using virtual plane, Medical physics, vol. 38, no. 12, pp. 6710-6720, 2011.
[37]F. Chassat, and S. Lavallée, Experimental protocol of accuracy evaluation of 6-D localizers for computer-integrated surgery: Application to four optical localizers, Medical Image Computing and Computer-Assisted Interventation—MICCAI’98, pp. 277-284: Springer, 1998.
[38]S. Schmerber, and M. Chassat, “Accuracy evaluation of a CAS system: laboratory protocol and results with 6D localizers, and clinical experiences in otorhinolaryngology, Computer Aided Surgery, vol. 6, no. 1, pp. 1-13, 2001.
[39]K. K. Shung, Diagnostic ultrasound: Imaging and blood flow measurements: CRC press, 2015.
[40]N. Otsu, “A threshold selection method from gray-level histograms, Automatica, vol. 11, no. 285-296, pp. 23-27, 1975.
[41]M. A. Wani, and B. G. Batchelor, “Edge-region-based segmentation of range images, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 16, no. 3, pp. 314-319, 1994.
[42]M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models, International journal of computer vision, vol. 1, no. 4, pp. 321-331, 1988.
[43]A. A. Amini, T. E. Weymouth, and R. C. Jain, “Using dynamic programming for solving variational problems in vision, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 12, no. 9, pp. 855-867, 1990.
[44]D. J. Williams, and M. Shah, “A fast algorithm for active contours and curvature estimation, CVGIP: Image understanding, vol. 55, no. 1, pp. 14-26, 1992.
[45]K.-M. Lam, and H. Yan, “Fast greedy algorithm for active contours, Electronics Letters, vol. 30, no. 1, pp. 21-23, 1994.