Intensity-vesselness Gaussian mixture model (IVGMM) for 2D + t segmentation of coronary arteries for X-ray angiography image sequences.

OBJECTIVE This study aimed to propose an intensity-vesselness Gaussian mixture model (IVGMM) tracking for 2D + t segmentation of coronary arteries for X-ray angiography (XA) image sequences. METHODS We compose a two dimensional (2D) feature vector of intensity and vesselness to characterize the Gaussian mixture models. In our IVGMM tracking, vessel segmentation is performed for each image frame based on these vessel and background IVGMMs and then the segmentation results of the current image frame is used to update these IVGMMs. The 2D + t segmentation of coronary arteries over the 2D XA image sequence is solved by means of iterating two processes, i.e., segmentation of coronary arteries and update of the IVGMMs. RESULTS The performance of the proposed IVGMM tracking was evaluated using clinical 2D XA datasets. We evaluated the segmentation accuracy of the IVGMM tracking by comparing with two previous 2D vessel segmentation methods and seven background subtraction (BGS) methods. Of the ten segmentation methods, IVGMM tracking shows the highest similarity to the manual segmentation in terms of precision, recall, Jaccard index (JI), F1 score, and peak signal-to-noise ratio (PSNR). CONCLUSIONS It is concluded that the IVGMM tracking could obtain reasonable segmentation accuracy outperforming conventional vessel enhancement methods and object tracking methods.

[1]  Bart M. ter Haar Romeny,et al.  A Novel 3D Multi-scale Lineness Filter for Vessel Detection , 2007, MICCAI.

[2]  I. Piña,et al.  ACCF/AHA/SCAI 2013 update of the clinical competence statement on coronary artery interventional procedures: a report of the American College of Cardiology Foundation/American Heart Association/American College of Physicians Task Force on Clinical Competence and Training (Writing Committee to Revise , 2013, Journal of the American College of Cardiology.

[3]  Nicolas Flasque,et al.  Acquisition, segmentation and tracking of the cerebral vascular tree on 3D magnetic resonance angiography images , 2001, Medical Image Anal..

[4]  Sheldon Goldberg,et al.  Percutaneous coronary intervention: historical perspectives, current status, and future directions. , 2003, American heart journal.

[5]  Robert J. Schalkoff,et al.  Pattern recognition : statistical, structural and neural approaches / Robert J. Schalkoff , 1992 .

[6]  Nassir Navab,et al.  Template-based CTA to x-ray angio rigid registration of coronary arteries in frequency domain with automatic x-ray segmentation. , 2013, Medical physics.

[7]  Horst Bischof,et al.  A Novel Approach for Detection of Tubular Objects and Its Application to Medical Image Analysis , 2008, DAGM-Symposium.

[8]  Nicholas Ayache,et al.  Model-Based Detection of Tubular Structures in 3D Images , 2000, Comput. Vis. Image Underst..

[9]  Jürgen Weese,et al.  Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images , 1997, CVRMed.

[10]  T. Peters,et al.  2D-3D registration of coronary angiograms for cardiac procedure planning and guidance. , 2005, Medical physics.

[11]  Stephen R. Aylward,et al.  Extracting branching tubular object geometry via cores , 2004, Medical Image Anal..

[12]  Theo van Walsum,et al.  Registration of $3{\rm D}+{\rm t}$ Coronary CTA and Monoplane $2{\rm D}+{\rm t}$ X-Ray Angiography , 2013, IEEE Transactions on Medical Imaging.

[13]  Jean-Marc Odobez,et al.  Multi-Layer Background Subtraction Based on Color and Texture , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[15]  Bernard J. Gersh,et al.  Fractional Flow Reserve versus Angiography for Guiding Percutaneous Coronary Intervention , 2010 .

[16]  Karl Rohr,et al.  Segmentation and Quantification of Human Vessels Using a 3-D Cylindrical Intensity Model , 2007, IEEE Transactions on Image Processing.

[17]  Albert C. S. Chung,et al.  Multi-scale model-based vessel enhancement using local line integrals , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  Badrinath Roysam,et al.  Robust 3-D Modeling of Vasculature Imagery Using Superellipsoids , 2007, IEEE Transactions on Medical Imaging.

[19]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[20]  Thierry Bouwmans,et al.  Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey , 2008 .

[21]  Antoine Vacavant,et al.  A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos , 2014, Comput. Vis. Image Underst..

[22]  Masato Otsuka,et al.  Utility of multislice computed tomography as a strategic tool for complex percutaneous coronary intervention , 2008, The International Journal of Cardiovascular Imaging.

[23]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Isabelle Bloch,et al.  A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes , 2009, Medical Image Anal..

[25]  Stephen R. Aylward,et al.  Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction , 2002, IEEE Transactions on Medical Imaging.

[26]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[27]  Milan Sonka,et al.  Automated segmentation of pulmonary vascular tree from 3D CT images , 2004, SPIE Medical Imaging.

[28]  Guido Gerig,et al.  Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images , 1998, Medical Image Anal..

[29]  Antonio Colombo,et al.  Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease. , 2009, The New England journal of medicine.

[30]  A. Jacobs,et al.  Percutaneous Coronary Intervention in the Current Era Compared With 1985 – 1986 , 2000 .

[31]  Nassir Navab,et al.  Deformable 2D-3D Registration of Vascular Structures in a One View Scenario , 2009, IEEE Transactions on Medical Imaging.

[32]  A. Jacobs,et al.  Percutaneous Coronary Intervention in the Current Era Compared With 1985–1986: The National Heart, Lung, and Blood Institute Registries , 2000, Circulation.

[33]  Max A. Viergever,et al.  Fast delineation and visualization of vessels in 3-D angiographic images , 2000, IEEE Transactions on Medical Imaging.

[34]  Antonio Colombo,et al.  The clinical outcome of percutaneous treatment of bifurcation lesions in multivessel coronary artery disease with the sirolimus-eluting stent: insights from the Arterial Revascularization Therapies Study part II (ARTS II). , 2007, European heart journal.

[35]  Patrick W Serruys,et al.  Computed tomography as a tool for percutaneous coronary intervention of chronic total occlusions. , 2010 .

[36]  H. Woodward.  Acquisition , 1979, Critical Inquiry.

[37]  Marleen de Bruijne,et al.  Statistical coronary motion models for 2D + t/3D registration of X-ray coronary angiography and CTA , 2013, Medical Image Anal..

[38]  Paul Suetens,et al.  Vesselness-based 2D–3D registration of the coronary arteries , 2009, International Journal of Computer Assisted Radiology and Surgery.

[39]  Patrick W Serruys,et al.  Value of preprocedure multislice computed tomographic coronary angiography to predict the outcome of percutaneous recanalization of chronic total occlusions. , 2005, The American journal of cardiology.

[40]  Hélène Laurent,et al.  Review and evaluation of commonly-implemented background subtraction algorithms , 2008, 2008 19th International Conference on Pattern Recognition.

[41]  Jack Lee,et al.  Automatic segmentation of 3D micro-CT coronary vascular images , 2007, Medical Image Anal..

[42]  Gerhard Rigoll,et al.  Background segmentation with feedback: The Pixel-Based Adaptive Segmenter , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[43]  M. Shah,et al.  Object tracking: A survey , 2006, CSUR.

[44]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[45]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[46]  Max A. Viergever,et al.  Multiscale vessel tracking , 2004, IEEE Transactions on Medical Imaging.

[47]  Calvin R. Maurer,et al.  A Linear Time Algorithm for Computing Exact Euclidean Distance Transforms of Binary Images in Arbitrary Dimensions , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[48]  Mohamed Cheriet,et al.  Nonrigid 2D/3D Registration of Coronary Artery Models With Live Fluoroscopy for Guidance of Cardiac Interventions , 2012, IEEE Transactions on Medical Imaging.

[49]  Robin Ferner.  Enhancement , 2012, BMJ : British Medical Journal.

[50]  Qiang Li,et al.  Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. , 2003, Medical physics.