Automatic 3-D tubular centerline tracking of coronary arteries in coronary computed tomographic angiography

AbstractPurpose: Coronary Computed Tomography Angiography (CCTA) is a promising alternative for high accuracy detection of a wide range of coronary artery diseases. To achieve the anatomical and pathological features of intramuscular coronary arteries with minimal user interaction, we need an automated coronary artery centerline extraction algorithm. Method: This article presents a fully automatic coronary artery centerline tracking algorithm. First, a complex continuous wavelet transform with the Gaussian kernels is used to reduce noise effect. Then, a multiple hypothesis tracking approach is applied to segment 3-D vessel structures. Finally, the tracking procedure is completed by applying a newly presented branch searching approach based on region growing algorithm and a mathematical morphology operation. Results: The performance of the presented method is measured on the publicly available Rotterdam Coronary Artery Algorithm Evaluation Framework. The extraction ability of the algorithm computed by over...

[1]  Damiana Lazzaro,et al.  Edge-preserving wavelet thresholding for image denoising , 2007 .

[2]  G. Placidi,et al.  Post-processing noise removal algorithm for magnetic resonance imaging based on edge detection and wavelet analysis. , 2003, Physics in medicine and biology.

[3]  Max A. Viergever,et al.  Vessel enhancing diffusion: A scale space representation of vessel structures , 2006, Medical Image Anal..

[4]  Rodrigo Minetto,et al.  Adaptive edge-preserving image denoising using wavelet transforms , 2013, Pattern Analysis and Applications.

[5]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Nicolas Passat,et al.  Fully automatic 3D segmentation of coronary arteries based on mathematical morphology , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[7]  Konstantin Mischaikow,et al.  Coronary vessel trees from 3D imagery: A topological approach , 2006, Medical Image Anal..

[8]  Ahmad Reza Naghsh-Nilchi,et al.  Cauchy Based Matched Filter for Retinal Vessels Detection , 2014, Journal of medical signals and sensors.

[9]  Rainer Raupach,et al.  Wavelet Based Noise Reduction in CT-Images Using Correlation Analysis , 2008, IEEE Transactions on Medical Imaging.

[10]  C. Fink,et al.  Coronary CT angiography: image quality, diagnostic accuracy, and potential for radiation dose reduction using a novel iterative image reconstruction technique—comparison with traditional filtered back projection , 2011, European Radiology.

[11]  Ahmad Reza Naghsh-Nilchi,et al.  Automatic vessel network features quantification using local vessel pattern operator , 2013, Comput. Biol. Medicine.

[12]  Giuseppe Placidi,et al.  A shape-based segmentation algorithm for X-ray digital subtraction angiography images , 2009, Comput. Methods Programs Biomed..

[13]  Marleen de Bruijne,et al.  Robust Shape Regression for Supervised Vessel Segmentation and its Application to Coronary Segmentation in CTA , 2011, IEEE Transactions on Medical Imaging.

[14]  Hossein Nezamabadi-pour,et al.  Image denoising in the wavelet domain using a new adaptive thresholding function , 2009, Neurocomputing.

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

[16]  Rakhi C. Motwani,et al.  Survey of Image Denoising Techniques , 2004 .

[17]  Heinz-Otto Peitgen,et al.  Multiple hypothesis template tracking of small 3D vessel structures , 2010, Medical Image Anal..

[18]  Ahmad Reza Naghsh-Nilchi,et al.  Noise tolerant local binary pattern operator for efficient texture analysis , 2012, Pattern Recognit. Lett..

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

[20]  Laurent D. Cohen,et al.  Fast extraction of minimal paths in 3D images and applications to virtual endoscopy , 2001, Medical Image Anal..

[21]  Ana Maria Mendonça,et al.  Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction , 2006, IEEE Transactions on Medical Imaging.

[22]  Martin Styner,et al.  Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms , 2009, Medical Image Anal..

[23]  Norberto F. Ezquerra,et al.  Automated segmentation of coronary vessels in angiographic image sequences utilizing temporal, spatial, and structural constraints , 1994, Other Conferences.

[24]  Yefeng Zheng,et al.  Robust and Accurate Coronary Artery Centerline Extraction in CTA by Combining Model-Driven and Data-Driven Approaches , 2013, MICCAI.

[25]  Konstantin Mischaikow,et al.  Coronary vessel cores from 3D imagery: a topological approach , 2005, SPIE Medical Imaging.

[26]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

[27]  T van Walsum,et al.  Coronary centerline extraction from CT coronary angiography images using a minimum cost path approach. , 2009, Medical physics.

[28]  Isabelle Bloch,et al.  Bayesian Maximal Paths for Coronary Artery Segmentation from 3D CT Angiograms , 2009, MICCAI.

[29]  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.

[30]  Yoshiro Kitamura,et al.  Automatic coronary extraction by supervised detection and shape matching , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[31]  Ahmad Reza Naghsh-Nilchi,et al.  Efficient Image Denoising Method Based on a New Adaptive Wavelet Packet Thresholding Function , 2012, IEEE Transactions on Image Processing.

[32]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[33]  Berend C. Stoel,et al.  Towards quantitative analysis of coronary CTA , 2005, The International Journal of Cardiovascular Imaging.

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

[35]  C. Lavie,et al.  Hispanics and cardiovascular health and the "Hispanic Paradox": what is known and what needs to be discovered? , 2014, Progress in cardiovascular diseases.

[36]  Jeroen J. Bax,et al.  Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography , 2011, The International Journal of Cardiovascular Imaging.

[37]  Ahmad Reza Naghsh-Nilchi,et al.  General rotation-invariant local binary patterns operator with application to blood vessel detection in retinal images , 2011, Pattern Analysis and Applications.

[38]  Ahmad Reza Naghsh-Nilchi,et al.  Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation , 2013, Biomed. Signal Process. Control..

[39]  Francis K. H. Quek,et al.  A review of vessel extraction techniques and algorithms , 2004, CSUR.

[40]  Demetri Terzopoulos,et al.  United Snakes , 1999, Medical Image Anal..

[41]  Sang Uk Lee,et al.  Robust segmentation of cerebral arterial segments by a sequential Monte Carlo method: Particle filtering , 2006, Comput. Methods Programs Biomed..

[42]  Jan Švihlík,et al.  Biomedical Image Volumes Denoising via the Wavelet Transform , 2011 .