(3.238.186.43) 您好!臺灣時間:2021/03/01 14:53
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:呂昆翰
研究生(外文):Kun-Han Lu
論文名稱:應用模糊能量之區域化形變模型於超音波影像中無形狀限制自動化血管偵測
論文名稱(外文):Automatic Vessel Detection without Shape Constraints from Ultrasound Images by Localized Fuzzy Energy-based Active Contour
指導教授:陳永耀陳永耀引用關係
指導教授(外文):Yung-Yaw Chen
口試委員:顏家鈺林文澧何明志連豊力
口試委員(外文):Jia-Yush YenWin-Li LinMing-Chih HoFeng-Li Lian
口試日期:2014-06-20
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:125
中文關鍵詞:超音波影像血管偵測無形狀限制快速區域化前置分割處理模糊能量基礎之形變模型
外文關鍵詞:Ultrasound imageVessel detectionWithout shape constraintsLocalized preliminary segmentationFuzzy energy-based active contour
相關次數:
  • 被引用被引用:0
  • 點閱點閱:88
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
超音波影像中之血管偵測可廣泛地應用於電腦輔助診斷系統,影像導引治療及不同醫學造影系統之間之影像對位。由於超音波影像常帶有過多的雜訊、不均質性和低對比度等干擾影像處理的因素,以往的血管偵測方法無法兼顧自動化及無形狀限制等要求,對於帶有模糊輪廓之血管也無法有效的分割出來。本論文提出一套自動化且強健之演算法,透過快速區域化前置分割處理來克服傳統形變模型無法處理不均質影像及收斂速度過慢之缺點,並利用其可以擷取帶有模糊邊緣物體之優點,進行局部血管輪廓之定位。不同於以往提出之方法,此新方法不需人為介入且沒有任何血管形狀上之限制。本論文研究結果顯示一張影像之平均處理時間約為0.197秒,血管偵測成功率達到89.4%。

Vessel detection from ultrasound images could be widely applied to computer aided diagnosis, image-guided online treatments and image registration between different imaging modalities. However, since the image quality of ultrasound images is often degraded by speckle noise, intensity inhomogeneity and low contrast, previous vessel detection approaches could not achieve the process of vessel detection automatically along with high accuracy and without certain shape constraints. Besides, they are not able to detect vessels with ambiguous boundary. In this thesis, a novel approach for detecting vessels automatically and robustly is proposed. Hence we propose a fast localized preliminary segmentation approach combined with fuzzy energy-based active contour so that it can deal with intensity inhomogeneity and it is able to segment objects with ambiguous boundary in one iteration. Apart from previous approaches, our approach does not require manual intervention and does not need a prior knowledge on the shape of vessels. The result of this study shows that we could process one frame with average processing time of 0.197 seconds and the overall accuracy of vessel detection is 89.4%.

口試委員會審定書 i
誌謝 ii
中文摘要 iii
ABSTRACT iv
CONTENTS v
LIST OF FIGURES viii
LIST OF TABLES xv
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Problem Formulation 3
1.3 Previous Work of Vessel Detection 4
1.4 Proposed Approach 7
1.5 Thesis Overview 8
Chapter 2 Study of Medical Ultrasound Image Processing 10
2.1 Introduction of Ultrasound Image Processing Techniques 11
2.1.1 Speckle Noise Reduction Techniques 13
2.1.2 Image Segmentation Techniques 21
2.1.3 Feature Extraction and Selection Techniques 30
2.1.4 Comparisons and Summary 33
2.2 Vessel Detection from B-mode Ultrasound Image 35
2.2.1 Semi-automatic Vessel Detection Approaches 36
2.2.2 Automatic Vessel Detection Approaches 39
2.2.3 Summary 43
Chapter 3 Automatic Hepatic Vessel Detection Approach 46
3.1 Redundant Artifact Removal 47
3.1.1 Elimination of Configurations 47
3.1.2 Side Shadow Removal 48
3.2 Bilateral Filtering 53
3.3 Preliminary Segmentation without Shape Constraints 59
3.3.1 Proposed Approach 59
3.3.2 Extraction of Parenchyma 64
3.3.3 Image Division 65
3.3.4 Extraction of Vessel Candidates 67
3.4 Fuzzy Energy-based Active Contour 71
3.5 Hepatic Vessel Classification 78
Chapter 4 Experimental Results 81
4.1 Experimental Setup 82
4.2 Results of Vessel Detection 83
4.3 Detection Rate 108
4.4 Summary 110
Chapter 5 Discussion and Comparisons 111
Chapter 6 Conclusions and Future Work 113
REFERENCES 114


[1]T. Lango, S. Vijayan, A. Rethy, C. Vapenstad, O. V. Solberg, R. Marvik, et al., "Navigated laparoscopic ultrasound in abdominal soft tissue surgery: technological overview and perspectives," International Journal of Computer Assisted Radiology and Surgery, vol. 7, pp. 585-599, 2012.
[2]T. Lange, N. Papenberg, S. Heldmann, J. Modersitzki, B. Fischer, H. Lamecker, et al., "3D ultrasound-CT registration of the liver using combined landmark-intensity information," International Journal of Computer Assisted Radiology and Surgery, vol. 4, pp. 79-88, 2009.
[3]C. Kirbas and F. Quek, "A review of vessel extraction techniques and algorithms," ACM Computing Surveys (CSUR), vol. 36, pp. 81-121, 2004.
[4]J. Guerrero, S. E. Salcudean, J. A. McEwen, B. A. Masri, and S. Nicolaou, "Real-time vessel segmentation and tracking for ultrasound imaging applications," IEEE Transactions on Medical Imaging, vol. 26, pp. 1079-1090, 2007.
[5]S. Milko, E. Samset, and T. Kadir, "Segmentation of the liver in ultrasound: a dynamic texture approach," International Journal of Computer Assisted Radiology and Surgery, vol. 3, pp. 143-150, 2008.
[6]A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, "Multiscale vessel enhancement filtering," in Medical Image Computing and Computer-Assisted Interventation—MICCAI, ed: Springer, pp. 130-137, 1998.
[7]V. Bettschart, B. Dagon, and C. Baur, "Real-Time Update of 3D Deformable Models for Computer Aided Liver Surgery," in 19Th International Conference On Pattern Recognition, Vols 1-6, pp. 2177-2180, 2008.
[8]G. P. Penney, J. M. Blackall, M. Hamady, T. Sabharwal, A. Adam, and D. J. Hawkes, "Registration of freehand 3D ultrasound and magnetic resonance liver images," Medical Image Analysis, vol. 8, pp. 81-91, 2004.
[9]C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images," in Sixth International Conference on Computer Vision, pp. 839-846, 1998.
[10]S. Krinidis and V. Chatzis, "Fuzzy energy-based active contours," IEEE Transactions on Image Processing, vol. 18, pp. 2747-2755, 2009.
[11]H. Cheng, J. Shan, W. Ju, Y. Guo, and L. Zhang, "Automated breast cancer detection and classification using ultrasound images: A survey," Pattern Recognition, vol. 43, pp. 299-317, 2010.
[12]S. K. Narayanan and R. Wahidabanu, "A view on despeckling in ultrasound imaging," International Journal of Signal Processing, Image Processing and Pattern Recognition ,vol. 2, no. 3, pp . 85–98 , 2009.
[13]P. Caloope, F. N. Medeiros, R. C. Marques, and R. C. Costa, "A comparison of filters for ultrasound images," in Telecommunications and Networking-ICT, ed: Springer, pp. 1035-1040, 2004.
[14]J.-S. Lee, "Digital image enhancement and noise filtering by use of local statistics," Pattern Analysis and Machine Intelligence, IEEE Transactions on, pp. 165-168, 1980.
[15]D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, "Adaptive noise smoothing filter for images with signal-dependent noise," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 165-177, 1985.
[16]V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. Holtzman, "A model for radar images and its application to adaptive digital filtering of multiplicative noise," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 157-166, 1982.
[17]D. T. Kuan, A. Sawchuk, T. C. Strand, and P. Chavel, "Adaptive restoration of images with speckle," IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 35, pp. 373-383, 1987.
[18]L. Weng, J. M. Reid, P. M. Shankar, and K. Soetanto, "Ultrasound speckle analysis based on the K distribution," The Journal of the Acoustical Society of America, vol. 89, pp. 2992-2995, 1991.
[19]P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, pp. 629-639, 1990.
[20]Y. Yu and S. T. Acton, "Speckle reducing anisotropic diffusion," IEEE Transactions on Image Processing, , vol. 11, pp. 1260-1270, 2002.
[21]X. Zong, A. F. Laine, and E. A. Geiser, "Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing," IEEE Transactions on Medical Imaging, vol. 17, pp. 532-540, 1998.
[22]X. Hao, S. Gao, and X. Gao, "A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing," IEEE Transactions on Medical Imaging, vol. 18, pp. 787-794, 1999.
[23]F. Abramovich, T. Sapatinas, and B. W. Silverman, "Wavelet thresholding via a Bayesian approach," Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 60, pp. 725-749, 1998.
[24]A. Achim, A. Bezerianos, and P. Tsakalides, "Novel Bayesian multiscale method for speckle removal in medical ultrasound images," IEEE Transactions on Medical Imaging, vol. 20, pp. 772-783, 2001.
[25]H. Xie, L. E. Pierce, and F. T. Ulaby, "SAR speckle reduction using wavelet denoising and Markov random field modeling," IEEE Transactions on Geoscience and Remote Sensing, vol. 40, pp. 2196-2212, 2002.
[26]D. Zha and T. Qiu, "A new algorithm for shot noise removal in medical ultrasound images based on alpha&;#8208;stable model," International Journal of Adaptive Control and Signal Processing, vol. 20, pp. 251-263, 2006.
[27]M. Forouzanfar, H. Moghaddam, and M. Dehghani, "Speckle reduction in medical ultrasound images using a new multiscale bivariate Bayesian MMSE-based method," in Signal Processing and Communications Applications, SIU,. IEEE 15th, pp. 1-4, 2007.
[28]N. Gupta, M. Swamy, and E. Plotkin, "Despeckling of medical ultrasound images using data and rate adaptive lossy compression," Medical Imaging, IEEE Transactions on, vol. 24, pp. 743-754, 2005.
[29]F. Zhang, Y. M. Yoo, K. L. Mong, and Y. Kim, "Nonlinear diffusion in laplacian pyramid domain for ultrasonic speckle reduction," IEEE Transactions on Medical Imaging, vol. 26, pp. 200-211, 2007.
[30]V. Behar, D. Adam, and Z. Friedman, "A new method of spatial compounding imaging," Ultrasonics, vol. 41, pp. 377-384, 2003.
[31]D. Adam, S. Beilin-Nissan, Z. Friedman, and V. Behar, "The combined effect of spatial compounding and nonlinear filtering on the speckle reduction in ultrasound images," Ultrasonics, vol. 44, pp. 166-181, 2006.
[32]P. F. Stetson, F. G. Sommer, and A. Macovski, "Lesion contrast enhancement in medical ultrasound imaging," IEEE Transactions on Medical Imaging, vol. 16, pp. 416-425, 1997.
[33]D. L. Pham, C. Xu, and J. L. Prince, "Current methods in medical image segmentation 1," Annual Review of Biomedical Engineering, vol. 2, pp. 315-337, 2000.
[34]J. A. Noble and D. Boukerroui, "Ultrasound image segmentation: a survey," Medical Imaging, IEEE Transactions on, vol. 25, pp. 987-1010, 2006.
[35]J. Noble, "Ultrasound image segmentation and tissue characterization," Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 224, pp. 307-316, 2010.
[36]K. Saini, M. Dewal, and M. Rohit, "Ultrasound Imaging and Image Segmentation in the area of Ultrasound: A Review," International Journal of Advanced Science &; Technology, vol. 24, pp. 41-60, 2010.
[37]A. Elnakib, G. Gimel’farb, J. S. Suri, and A. El-Baz, "Medical image segmentation: A brief survey," in Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies, ed: Springer, pp. 1-39, 2011.
[38]H.-D. Cheng, X. Jiang, Y. Sun, and J. Wang, "Color image segmentation: advances and prospects," Pattern recognition, vol. 34, pp. 2259-2281, 2001.
[39]R. N. Czerwinski, D. L. Jones, and W. O''Brien, "Detection of lines and boundaries in speckle images-application to medical ultrasound," IEEE Transactions on Medical Imaging, vol. 18, pp. 126-136, 1999.
[40]R.-F. Chang, K.-C. Chang, H.-J. Chen, D.-R. Chen, E. Takada, and W. Kyung Moon, "Whole breast computer-aided screening using free-hand ultrasound," in International Congress Series, pp. 1075-1080, 2005.
[41]K. Horsch, M. L. Giger, L. A. Venta, and C. J. Vyborny, "Automatic segmentation of breast lesions on ultrasound," Medical Physics, vol. 28, pp. 1652-1659, 2001.
[42]X. Hao, C. Bruce, C. Pislaru, and J. F. Greenleaf, "A novel region growing method for segmenting ultrasound images," in Ultrasonics Symposium, IEEE, pp. 1717-1720, 2000.
[43]C.-M. Chen, Horng-Shing Lu, and Y.-L. Chen, "A discrete region competition approach incorporating weak edge enhancement for ultrasound image segmentation," Pattern recognition letters, vol. 24, pp. 693-704, 2003.
[44]S. Poonguzhali and G. Ravindran, "A complete automatic region growing method for segmentation of masses on ultrasound images," in International Conference on Biomedical and Pharmaceutical Engineering, ICBPE, pp. 88-92, 2006.
[45]O. V. Solberg, F. Lindseth, H. Torp, R. E. Blake, and T. A. Nagelhus Hernes, "Freehand 3D ultrasound reconstruction algorithms—a review," Ultrasound in Medicine &; Biology, vol. 33, pp. 991-1009, 2007.
[46]M. Baumhauer, M. Feuerstein, H.-P. Meinzer, and J. Rassweiler, "Navigation in endoscopic soft tissue surgery: perspectives and limitations," Journal of Endourology, vol. 22, pp. 751-766, 2008.
[47]B. C. Patel and D. G. Sinha, "An adaptive K-means clustering algorithm for breast image segmentation," International Journal of Computer Applications, vol. 10, pp. 35-38, 2010.
[48]A. R. Abdel-Dayem and M. R. El-Sakka, "Fuzzy c-means clustering for segmenting Carotid Artery Ultrasound Images," in Image Analysis and Recognition, ed: Springer, pp. 935-948, 2007.
[49]A.-C. Liew, S. H. Leung, and W. H. Lau, "Segmentation of color lip images by spatial fuzzy clustering," IEEE Transactions on Fuzzy Systems, vol. 11, pp. 542-549, 2003.
[50]G. Xiao, M. Brady, J. A. Noble, and Y. Zhang, "Segmentation of ultrasound B-mode images with intensity inhomogeneity correction," Medical Imaging, IEEE Transactions on, vol. 21, pp. 48-57, 2002.
[51]L. A. Christopher, E. J. Delp, C. R. Meyer, and P. L. Carson, "3-D Bayesian ultrasound breast image segmentation using the EM/MPM algorithm," in International Symposium on Biomedical Imaging, pp. 86-89, 2002.
[52]D. Boukerroui, O. Basset, N. Guerin, and A. Baskurt, "Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation," European Journal of Ultrasound, vol. 8, pp. 135-144, 1998.
[53]M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," International Journal of Computer Vision, vol. 1, pp. 321-331, 1988.
[54]C. Li, C.-Y. Kao, J. C. Gore, and Z. Ding, "Implicit active contours driven by local binary fitting energy," in IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1-7, 2007.
[55]O. Michailovich and A. Tannenbaum, "Segmentation of medical ultrasound images using active contours," IEEE International Conference on Image Processing, ICIP, pp. 513-516, 2007.
[56]K. Zhang, H. Song, and L. Zhang, "Active contours driven by local image fitting energy," Pattern recognition, vol. 43, pp. 1199-1206, 2010.
[57]C. L. Pereira, C. A. Bastos, T. I. Ren, and G. D. Cavalcanti, "Fuzzy Active Contour Models," IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1621-1627, 2011.
[58]W. HU, Z. LI, and Z. LIU, "A Novel Local Energy-based With Fuzzy Clustering Active Contours for Image Segmentation," Journal of Computational Information Systems, vol. 8, pp. 2991-2998, 2012.
[59]K.-K. Shyu, V.-T. Pham, T.-T. Tran, and P.-L. Lee, "Global and local fuzzy energy-based active contours for image segmentation," Nonlinear Dynamics, vol. 67, pp. 1559-1578, 2012.
[60]K.-K. Shyu, T.-T. Tran, V.-T. Pham, P.-L. Lee, and L.-J. Shang, "Fuzzy distribution fitting energy-based active contours for image segmentation," Nonlinear Dynamics, vol. 69, pp. 295-312, 2012.
[61]Y. Wang and C. He, "Image segmentation algorithm by piecewise smooth approximation," EURASIP Journal on Image and Video Processing, vol. 2012, pp. 1-13, 2012.
[62]J. Yuan, "Active contour driven by local divergence energies for ultrasound image segmentation," IET Image Processing, vol. 7, pp. 252-259, 2013.
[63]D.-R. Chen, R.-F. Chang, W.-J. Wu, W. K. Moon, and W.-L. Wu, "3-D breast ultrasound segmentation using active contour model," Ultrasound in Medicine &; Biology, vol. 29, pp. 1017-1026, 2003.
[64]T. F. Chan and L. A. Vese, "Active contours without edges," IEEE transactions on Image processing, vol. 10, pp. 266-277, 2001.
[65]K. Drukker, M. L. Giger, C. J. Vyborny, and E. B. Mendelson, "Computerized detection and classification of cancer on breast ultrasound," Academic radiology, vol. 11, pp. 526-535, 2004.
[66]Y.-L. Huang and D.-R. Chen, "Support vector machines in sonography: application to decision making in the diagnosis of breast cancer," Clinical imaging, vol. 29, pp. 179-184, 2005.
[67]Y.-L. Huang, K.-L. Wang, and D.-R. Chen, "Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines," Neural Computing &; Applications, vol. 15, pp. 164-169, 2006.
[68]D.-R. Chen, R.-F. Chang, W.-J. Kuo, M.-C. Chen, and Y.-L. Huang, "Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks," Ultrasound in medicine &; biology, vol. 28, pp. 1301-1310, 2002.
[69]W.-J. Kuo, R.-F. Chang, C. C. Lee, W. K. Moon, and D.-R. Chen, "Retrieval technique for the diagnosis of solid breast tumors on sonogram," Ultrasound in Medicine &; Biology, vol. 28, pp. 903-909, 2002.
[70]R.-F. Chang, W.-J. Wu, W. K. Moon, and D.-R. Chen, "Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors," Breast Cancer Research and Treatment, vol. 89, pp. 179-185, 2005.
[71]T. Lindeberg, "Detecting salient blob-like image structures and their scales with a scale-space primal sketch: a method for focus-of-attention," International Journal of Computer Vision, vol. 11, pp. 283-318, 1993.
[72]N. Otsu, "A threshold selection method from gray-level histograms," Automatica, vol. 11, pp. 23-27, 1975.
[73]R. Adams and L. Bischof, "Seeded region growing," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 16, pp. 641-647, 1994.
[74]R. F. Wagner, S. W. Smith, J. M. Sandrik, and H. Lopez, "Statistics of speckle in ultrasound B-scans," IEEE TRANS. SONICS ULTRASONICS., vol. 30, pp. 156-163, 1983.
[75][: Liver, vessels, color doppler (video) ]
Liver, vessels, color doppler (video). In Samsung Medison Website. Retrieved from http://www.medison.ru/.
[76][:ATL CT8-4 Probe ]
ATL CT8-4 Probe. In MedCorp Website. Retrieved from http://www.medcorpllc.com/.


QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔