Segmentation method of intravascular ultrasound images of human coronary arteries

The goal of this study was to show the feasibility of a 2D segmentation fast-marching method (FMM) in the context of intravascular ultrasound (IVUS) imaging of coronary arteries. The original FMM speed function combines gradient-based contour information and region information, that is the gray level probability density functions of the vessel structures, that takes into account the variability in appearance of the tissues and the lumen in IVUS images acquired at 40 MHz. Experimental results on 38 in vivo IVUS sequences yielded mean point-to-point distances between detected vessel wall boundaries and manual validation contours below 0.11 mm, and Hausdorff distances below 0.33 mm, as evaluated on 3207 images. The proposed method proved to be robust in taking into account various artifacts in ultrasound images: partial shadowing due to calcium inclusions within the plaque, side branches adjacent to the main artery to segment, the presence of a stent, injection of contrast agent or dissection, as tested on 209 images presenting such artifacts.

[1]  A probabilistic segmentation method for the identification of luminal borders in intravascular ultrasound images , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  J A Sethian,et al.  A fast marching level set method for monotonically advancing fronts. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[3]  David Moratal,et al.  Automatic segmentation and 3D reconstruction of intravascular ultrasound images for a fast preliminar evaluation of vessel pathologies , 2007, Comput. Medical Imaging Graph..

[4]  Septimiu E. Salcudean,et al.  Real-Time Vessel Segmentation and Tracking for Ultrasound Imaging Applications , 2007, IEEE Transactions on Medical Imaging.

[5]  Nassir Navab,et al.  Automatic segmentation of calcified plaques and vessel borders in IVUS images , 2008, International Journal of Computer Assisted Radiology and Surgery.

[6]  H Ermert,et al.  Segmentation of 3D intravascular ultrasonic images based on a random field model. , 2000, Ultrasound in medicine & biology.

[7]  Ioannis Kompatsiaris,et al.  Image analysis techniques for automated IVUS contour detection. , 2008, Ultrasound in medicine & biology.

[8]  Jean Meunier,et al.  Segmentation of Plaques in Sequences of Ultrasonic B-Mode Images of Carotid Arteries Based on Motion Estimation and a Bayesian Model , 2011, IEEE Transactions on Biomedical Engineering.

[9]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Petia Radeva,et al.  Statistical strategy for anisotropic adventitia modelling in IVUS , 2006, IEEE Transactions on Medical Imaging.

[11]  Aleksandra Mojsilović,et al.  Automatic segmentation of intravascular ultrasound images: A texture-based approach , 1997, Annals of Biomedical Engineering.

[12]  Johan H. C. Reiber,et al.  Multi-agent segmentation of IVUS images , 2004, Pattern Recognit..

[13]  Wenguang Li,et al.  Image and Signal Processing in Intravascular Ultrasound , 1997 .

[14]  Dimitrios I. Fotiadis,et al.  An automated method for lumen and media-adventitia border detection in a sequence of IVUS frames , 2004, IEEE Transactions on Information Technology in Biomedicine.

[15]  P. Fitzgerald,et al.  Automated contour detection for high-frequency intravascular ultrasound imaging: a technique with blood noise reduction for edge enhancement. , 2000, Ultrasound in medicine & biology.

[16]  Johan H. C. Reiber,et al.  User-Agent Cooperation in Multiagent IVUS Image Segmentation , 2009, IEEE Transactions on Medical Imaging.

[17]  Sérgio Shiguemi Furuie,et al.  Automatic coronary wall segmentation in intravascular ultrasound images using binary morphological reconstruction. , 2011, Ultrasound in medicine & biology.

[18]  Milan Sonka,et al.  Plaque development, vessel curvature, and wall shear stress in coronary arteries assessed by X-ray angiography and intravascular ultrasound , 2006, Medical Image Anal..

[19]  Milan Sonka,et al.  Segmentation of intravascular ultrasound images: a knowledge-based approach , 1995, IEEE Trans. Medical Imaging.

[20]  Jean Meunier,et al.  Segmentation in Ultrasonic B-Mode Images of Healthy Carotid Arteries Using Mixtures of Nakagami Distributions and Stochastic Optimization , 2009, IEEE Transactions on Medical Imaging.

[21]  Marie-Hélène Roy Cardinal,et al.  Fast-marching segmentation of three-dimensional intravascular ultrasound images: a pre- and post-intervention study. , 2010, Medical physics.

[22]  Petia Radeva,et al.  ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences , 2009, MICCAI.

[23]  Andrew H. Gee,et al.  Decompression and speckle detection for ultrasound images using the homodyned k-distribution , 2003, Pattern Recognit. Lett..

[24]  Georgios Tziritas,et al.  Bayesian Level Sets for Image Segmentation , 2002, J. Vis. Commun. Image Represent..

[25]  G Kovalski,et al.  Three-dimensional automatic quantitative analysis of intravascular ultrasound images. , 2000, Ultrasound in medicine & biology.

[26]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[27]  J. Greenleaf,et al.  Ultrasound Echo Envelope Analysis Using a Homodyned K Distribution Signal Model , 1994 .

[28]  Jean Meunier,et al.  Intravascular ultrasound image segmentation: a three-dimensional fast-marching method based on gray level distributions , 2006, IEEE Transactions on Medical Imaging.

[29]  Nassir Navab,et al.  A State-of-the-Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images , 2012, IEEE Transactions on Information Technology in Biomedicine.

[30]  Joachim M. Buhmann,et al.  On learning texture edge detectors , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[31]  Hemant D. Tagare,et al.  Evaluation of Four Probability Distribution Models for Speckle in Clinical Cardiac Ultrasound Images , 2006, IEEE Transactions on Medical Imaging.

[32]  D. Vince,et al.  Evaluation of three-dimensional segmentation algorithms for the identification of luminal and medial-adventitial borders in intravascular ultrasound images , 2000, IEEE Transactions on Medical Imaging.

[33]  Colin Berry,et al.  Comparison of Intravascular Ultrasound and Quantitative Coronary Angiography for the Assessment of Coronary Artery Disease Progression , 2007, Circulation.

[34]  J. Reiber,et al.  Advanced contour detection for three-dimensional intracoronary ultrasound: a validation – in vitro and in vivo , 2002, The International Journal of Cardiovascular Imaging.

[35]  Petia Radeva,et al.  Automatic segmentation of artery wall in coronary IVUS images: a probabilistic approach , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[36]  A. Wahle,et al.  Angiography and Intravascular Ultrasound , 2000 .

[37]  Milan Sonka,et al.  Tissue characterization in intravascular ultrasound images , 1998, IEEE Transactions on Medical Imaging.

[38]  Michael G. Strintzis,et al.  A novel active contour model for fully automated segmentation of intravascular ultrasound images: In vivo validation in human coronary arteries , 2007, Comput. Biol. Medicine.

[39]  G. Cloutier,et al.  A critical review and uniformized representation of statistical distributions modeling the ultrasound echo envelope. , 2010, Ultrasound in medicine & biology.

[40]  Frits Mastik,et al.  Fully automatic luminal contour segmentation in intracoronary ultrasound imaging-a statistical approach , 2004, IEEE Transactions on Medical Imaging.

[41]  Zheng Sun,et al.  A Parallel Method for Segmenting Intravascular Ultrasound Image Sequence , 2011 .

[42]  E. Gerardo Mendizabal-Ruiz,et al.  Computerized Medical Imaging and Graphics , 2022 .

[43]  P. Shankar.  A general statistical model for ultrasonic backscattering from tissues , 2000 .

[44]  C J Slager,et al.  Morphometric analysis in three-dimensional intracoronary ultrasound: an in vitro and in vivo study performed with a novel system for the contour detection of lumen and plaque. , 1996, American heart journal.

[45]  P. Yock,et al.  Intravascular ultrasound: novel pathophysiological insights and current clinical applications. , 2001, Circulation.

[46]  E. Gerardo Mendizabal-Ruiz,et al.  Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach , 2013, Medical Image Anal..

[47]  Elsa D. Angelini,et al.  Automatic detection of luminal borders in IVUS images by magnitude-phase histograms of complex brushlet coefficients , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[48]  Petia Radeva,et al.  HoliMAb: A holistic approach for Media-Adventitia border detection in intravascular ultrasound , 2012, Medical Image Anal..

[49]  Livia Kapusta,et al.  Modeling envelope statistics of blood and myocardium for segmentation of echocardiographic images. , 2008, Ultrasound in medicine & biology.

[50]  Tong Fang,et al.  Shape-Driven Segmentation of the Arterial Wall in Intravascular Ultrasound Images , 2008, IEEE Transactions on Information Technology in Biomedicine.

[51]  R. F. Wagner,et al.  Statistics of Speckle in Ultrasound B-Scans , 1983, IEEE Transactions on Sonics and Ultrasonics.