연구동향 분석
박사

광류 검출 기법을 이용한 조영 증강 초음파 영상 내 조영제 유동 정량화 연구 = Quantification of Contrast Agent Flow Patterns from Contrast-enhanced Ultrasound Images Using an Optical Flow Estimation Technique

이주환 2015년

논문상세정보
인용/피인용
' 광류 검출 기법을 이용한 조영 증강 초음파 영상 내 조영제 유동 정량화 연구 = Quantification of Contrast Agent Flow Patterns from Contrast-enhanced Ultrasound Images Using an Optical Flow Estimation Technique' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • contrast agent
  • flow pattern
  • optical flow
  • ultrasound
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
340 0

0.0%

' 광류 검출 기법을 이용한 조영 증강 초음파 영상 내 조영제 유동 정량화 연구 = Quantification of Contrast Agent Flow Patterns from Contrast-enhanced Ultrasound Images Using an Optical Flow Estimation Technique' 의 참고문헌

  • “초음파 진단 방식”
    나승권 메디저널 [2008]
  • “초음파 영상학”
  • “초음파 영상에서의 Optical Flow추적 성능 향상을 위한 전처리 알고리즘 개발 연구”
    김성민 노승규 박성윤 이주환 대한전자공학회논문지, 47(5), 24-32 [2010]
  • “조영 증강 초음파의 신경 초음파 분야에서의 적용”
    서우근 Journalof Neurosonology, 4(1), 8-13 [2012]
  • T. Gautama, M. M. van Hulle, “A phase-based approach to theestimation of the optical flow field using spatial filtering”, IEEETransactions on Neural Networks, 13(5), 1127-1136, 2002.
  • T. Albrecht, M. Blomley, L. Bolondi, M. Claudon, J. M. Correas, D.Cosgrove, L. Greiner, K. J?ger, N. D. Jong, E. Leen, R. Lencioni, D.Lindsell, A. Martegani, L. Solbiati, L. Thorelius, F. Tranguart, H.P. Weskott, T. Whittingham, “Guidelines for the use of contrastagents in ultrasound. January 2004”, Ultraschall in der Medizin,25(4), 249-256, 2004.149
  • Signal and Image Processing Institute, Ming Hsieh Department ofElectrical Engineering, University of Southern California.153
  • S. H. Lai, “Computation of optical flow under non-uniformbrightness variations”, Pattern Recognition Letters, 25(8), 885-892,2004.
  • S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black, R.Szeliski, “A database and evaluation methodology for optical flow”,International Journal of Computer Vision, 92(1), 1-31, 2011.
  • P. Thevenaz, U. E. Ruttimann, M. Unser, “A pyramid approach tosubpixel registration based on intensity”, IEEE Transactions onImage Processing, 7(1), 27-41, 1998.
  • P. Perona, J. Malik, “Scale-space and edge detection usinganisotropic diffusion”, in Proc. IEEE CSWCV, 16-22, 1987.
  • P. Perona, J. Malik, “Scale-space and edge detection usinganisotropic diffusion”, IEEE Transactions on Pattern Analysis andMachine Intelligence, 12(7), 629-639, 1990.
  • P. J. Burt, C. Yen, X. Xu, “Multiresolution flow-through motionanalysis”, in Proc. IEEE CVPR, Washington, 246-252, 1983.
  • P. Anandan, “A computational framework and an algorithm for themeasurement of visual motion”, International Journal of ComputerVision, 2(3), 283-310, 1989.
  • N. Otsum “A threshold selection method from gray-levelhistogram”, IEEE Transactions on System Man Cybernetic,SMC-9(1), 62-66, 1979.
  • N. Malpica, A. Santos, M. A. Zuluaga, M. J. Ledesma, E. Perez,M. A. Garcia-Fernandez, M. Desco, “Tracking ofregions-of-interest in myocardial contrast echocardiography”,Ultrasound in Medicine and Biology, 30(3), 303-309, 2004.
  • M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, H.Bischof, “Anisotropic Huber-L1 optical flow”, in Proc. BMVC,108-1-108-11, 2009.
  • M. S?hling, M. Arigovindan, C. Jansen, P. Hunziker, M. Unser,“Myocardial motion analysis from B-mode echocardiograms”, IEEETransactions on Image Processing, 14(4), 525-536, 2005.
  • M. Otte, H. H. Nagel, “Optical flow estimation: advances andcomparisons”, in Proc. ECCV, 49-60, 1994.
  • M. J. Black, P. Anandan, “The robust estimation of multiplemotions: parametric and piecewise-smooth flow fields”, ComputerVision and Image Understanding, 63(1), 75-104, 1996.
  • M. Elad, “On the origin of the bilateral filter and ways to improveit”, IEEE Transactions on Image Processing, 11(10), 1141-1151,2002.
  • M. Drulea, S. Nedevschi, “Total variation regularization ofloca-global optical flow”, in Proc. ICITS, 318-323, 2011.
  • M. D?nil?, A. Popescu, R. ?irli, I. Sporea, A. Martie, M. ?endroiu,“Contrast enhanced ultrasound (CEUS) in the evaluation of livermetastases”, Medical Ultrasonography, 12(3), 233-237, 2010.
  • L. I. Rudin, S. Osher, E. Fatemi, “Nonlinear total variation basednoise removal algorithms”, Physica D: Nonlinear Phenomena,60(1-4), 259-268, 1992.
  • L. Haglund, “Adaptive multidimensional filtering”, Ph. D.Dissertation, Department of Electrical Engineering, University ofLinkoping, 1992.
  • J. Stoitsis, S. Golemati, K. S. Nikita, “A modular software systemto assist interpretation of medical images-application to vascularultrasound images”, IEEE Transactions on Instrumentation andMeasurement, 55(6), 1944-1952, 2006.
  • J. P. van Santen, G. Sperling, “Elaborated reichardt detectors”,Journal of the Optical Society of America, 2(2), 300-321, 1985.
  • J. L. Barron, D. J. Fleet, S. S. Beauchemin, “Performance ofoptical flow techniques”, International Journal of Computer Vision,12(1), 43-77, 1994.
  • J. J. Little, H. H. Bulthoff, T. A. Poggio, “Parallel optical flowusing local voting”, in Proc. IEEE ICCV, Tampa, 454-459, 1988.
  • J. J. Little, A. Verri, “Analysis of differential and matchingmethods for optical flow”, in Proc. IEEE Workshop on VisualMotion, Irvine CA, 173-180, 1989.
  • J. H. Lee, Y. N. Hwang, S. Y. Park, J. S. Jeong, S. M. Kim, “Ananalysis of contrast agent flow patterns from sequential ultrasoundimages using a motion estimation algorithm based on optical flowpatterns”, IEEE Transactions on Biomedical Engineering, 62(1),49-59, 2015.
  • J. H. Lee, S. Y. Park, S. J. Kim, S. M. Kim, “The study ofphase-based optical flow technique using an adaptive bilateralfilter”, IEICE Transactions on Information and Systems, E95(2),658-667, 2012.
  • J. H. Lee, S. M. Kim, “Estimating contrast agent motion fromultrasound images using an anisotropic diffusion-based optical flowtechnique”, Computers in Biology and Medicine, 43(11), 1853-1862,2013.
  • J. F. Aujol, G. Gilboa, T. Chan, S. Osher, “Structure-textureimage decomposition-modeling, algorithms, and parameterselection”, International Journal of Computer Vision, 67(1), 111-136,2006.
  • J. Canny, “A computational approach to edge detection”, IEEETransactions on Pattern Analysis and Machine Intelligence, 8(6),679-698, 1986.
  • J. Bigun, G. Granlund, J. Wiklund, “Multidimensional orientationestimation with applications to texture analysis and optical flow”,IEEE Transactions on Pattern Analysis and Machine Intelligence,13(8), 775-790, 1991.
  • H. H. Nagel, “Displacement vectors derived from second-orderintensity variations in image sequences”, Computer Vision,Graphics, and Image Processing, 21(1), 85-117, 1983.
  • H. Freigenbaum, J. M. Stone, D. A. Lee, W. K. Nasser, S. Chang,“Identification of ultrasound echoes from the left ventricle by useof intracardiac injections of indocyanine green”, Circulation, 41(4),615-621, 1970.
  • H. Carr, J. Snoeyink, M. V. D. Panne, "Simplifying FlexibleIsosurfaces Using Local Geometric Measures", IEEE Visualization,pp. 497-504, 2004
  • H. Barman, L. Haglund, H. Knutsson, G. Granlund, “Estimation ofvelocity, acceleration and disparity in time sequences”, in Proc.IEEE Workshop on Visual Workshop, Princeton, 44-51, 1991.
  • G. Sapiro, “Geometric partial differential equations and imageanalysis”, Cambridge University Press, 223, 2001.
  • F. Tranguart, L. Mercier, P. Frinking, E. Gaud, and M. Arditi,“Perfusion quantification in contrast-enhanced ultrasound (CEUS) -Ready for research projects and routine clinical use”, Ultraschall inder Medizin, 33(1), 31-s38, 2012.
  • F. L. Giesel, P. Flechsig, T. Kuder, L. H. Schwartz, S. Wulfert, C.M. Zechmannm, F. Bruchertseifer, U. Haberkorn, C. Kratochwil,“Contrast-enhanced ultrasound monitoring of perfusion changes inhepatic neuroendocrine metastases after systemic versus selectivearterial 177Lu/90Y-DOTATOC and 213Bi-DOTATOC radiopeptidetherapy”, Experimental Oncology, 35(2), 122-126, 2013.
  • F. Glazer, G. Reynolds, P. Anandan, “Scene matching throughhierarchical correlation”, in Proc. IEEE CVPR, Washington, 432-441,1983.150
  • E. P. Simoncelli, “Distributed representation and analysis of visualmotion”, Ph. D. Dissertation, Department of Electrical Engineeringand Computer Science, MIT, 1993.
  • E. H. Adelson, J. R. Bergen, “The extraction of spatiotemporalenergy in human and machine vision”, in Proc. IEEE Workshop onVisual Motion, Charleston, 151-156, 1986.
  • E. H. Adelson, J. R. Bergen, “Spatiotemporal energy models forthe perception of motion”, Journal of the Optical Society ofAmerica, 2(2), 284-299, 1985.
  • E. H. Adelson, J. A. Movshon, “Phenomenal coherence of movingvisual patterns”, Nature, 300(5892), 523-525, 1982.
  • D. Sun, S. Roth, M. J. Black, “Secrets of optical flow estimationand their principles”, in Proc. IEEE CCVPR, 2432-2439, 2010.154
  • D. J. Heeger, “Optical flow using spatiotemporal filters”,International Journal of Computer Vision, 1(4), 279-302, 1988.
  • D. J. Fleet, “Measurement of image velocity”, Kluwer AcademicPublishers, Norwell, 1992.
  • D. J. Fleet, A. D. Jepson, “Computation of component imagevelocity from loca lphase information”, International Journal ofComputer Vision, 5(1), 77-104, 1990.
  • D. H. Warren, E. R. Strelow, “Electronic spatial sensing for theblind: contributions from perception”, Springer, 1985.
  • D. H. Douglas, T. K. Peucker, “Algorithms for the Reduction of theNumber of Points Required to Represent a Digitized Line or ItsCaricature”, The Canadian Cartographer, Vol. 10, no. 2, pp. 112-122, 1973152
  • D. Cosgrove, “Ultrasound contrast agents: An overview”, EuropeanJournal of Radiology, 60(3), 324-330, 2006.
  • C. Zach, T. Pock, H. Bischof, “A duality based approach forrealtime TV-L1 optical flow”, in Proc. DAGM CPR, 214-223, 2007.155
  • C. Tomasi, R. Manduchi, “Bilateral filtering for gray and colorimages”, in Proc. IEEE ICCV, India, 839-846, 1998.
  • C. L. Fennema, “Velocity determination in scenes containingseveral moving objects”, Computer Graphics and Image Processing,9(4), 301-315, 1979.
  • B. Zhang, J. P. Allebach, “Adaptive bilateral filter for sharpnessenhancement and noise removal”, IEEE Transactions on ImageProcessing, 17(5), 664-678, 2008.
  • B. K. P. Horn, B. G. Schunck, “Determining optical flow”,Artificial Intelligence, 17(1-3), 185-203, 1981.
  • B. Jahne, “Image sequence analysis of complex physical objects:nonlinear small scale water surface waves”, in Proc. IEEE ICCV,London, 191-200, 1987.151
  • B. D. Lucas, “Generalized image matching by the method ofdifferences”, Doctoral Dissertation, Robotics Institute, CarnegieMellon University, 1984.
  • B. D. Lucas, T. Kanade, “An iterative image registration techniquewith an application to stereo vision”, in Proc. DARPA IUWorkshop, 121-130, 1981
  • A. Wong, “Adaptive bilateral filtering of image signals using locaphase characteristics”, Signal Processing, 88(6), 1615-1619, 2008.
  • A. Wedel, T. Pock, C. Zach, H. Bischof, D. Cremers, “Animproved algorithm for TV-L1 optical flow”, Lecture Notes inComputer Science 5604, 23-45, 2009.
  • A. Gastounioti, N. N. Tsiaparas, S. Golemati, J. S. Stoitsis, K. S.Nikita, “Affine optical flow combined with multiscale imageanalysis for motion estimation of the arterial wall from B-modeultrasound”, in Proc. IEEE EMBC, 559-562, 2011.
  • A. Chambolle, “An algorithm for total variation minimization andapplications”, Journal of Mathematical Imaging and Vision, 20(1-2),89-97, 2004.
  • A. Burton, J. Radford, “Thinking in perspective: critical essays inthe study of thought processes”, Routledge, 1978.
  • A. Bruhn, J. Weickert, C. Schn?rr, “Combining the advantages oflocal and global optic flow methods”, in Proc. SPR, 454-462, 2002.
  • A. B. Watson, A. J. Ahumada, “Model of human visual-motionsensing”, Journal of the Optical Society of America, 2(2), 322-341,1985.