On the segmentation of vascular geometries from medical images

A comprehensive analysis of vascular morphology and the application of generic models of vascular biomechanics to specific patients require the ability of extracting a geometrical representation of the vascular anatomy from medical images. Owing to the wide range of clinical manifestations of vascular disease and associated imaging modalities and protocols, several segmentation methods have been proposed over the last 20 years and are available in the literature. In this paper, we review the methods of segmentation of angiographic medical images and identify major advantages and disadvantages of state-of-the-art techniques. We further discuss the performance of some of the most popular intensity-based and gradient-based methods using a set of images of peripheral by-pass grafts acquired with magnetic resonance angiography (MRA). We then propose a threshold front method for the segmentation of MRA images and assess its performance using two anatomic scale replica models, reproducing a normal and a stenotic peripheral artery. The threshold front algorithm is a simple, fast and parameter-free (still adaptive) method achieving segmentation errors below pixel resolution. Copyright © 2009 John Wiley & Sons, Ltd.

[1]  Alejandro F. Frangi,et al.  Three-dimensional modeling for functional analysis of cardiac images, a review , 2001, IEEE Transactions on Medical Imaging.

[2]  Guillermo Sapiro,et al.  From active contours to anisotropic diffusion: connections between basic PDE's in image processing , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[3]  N. Ayache.  L'analyse automatique des images médicales État de l'art et perspectives , 1998 .

[4]  James S. Duncan,et al.  Bending and stretching models for LV wall motion analysis from curves and surfaces , 1992, Image Vis. Comput..

[5]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[6]  Dan Givoli,et al.  An adaptive finite element procedure for the image segmentation problem , 1998 .

[7]  Joaquim Peiró,et al.  Three-dimensional reconstruction of autologous vein bypass graft distal anastomoses imaged with magnetic resonance: clinical and research applications. , 2003, Journal of vascular surgery.

[8]  Alejandro F. Frangi,et al.  Image intensity standardization in 3D rotational angiography and its application to vascular segmentation , 2008, SPIE Medical Imaging.

[9]  P. Lions,et al.  Axioms and fundamental equations of image processing , 1993 .

[10]  Y. J. Zhang,et al.  A survey on evaluation methods for image segmentation , 1996, Pattern Recognit..

[11]  Jean-Michel Morel,et al.  A review of P.D.E. models in image processing and image analysis , 2002 .

[12]  J. Alison Noble,et al.  Ultrasound image segmentation: a survey , 2006, IEEE Transactions on Medical Imaging.

[13]  J. Alison Noble,et al.  An adaptive segmentation algorithm for time-of-flight MRA data , 1999, IEEE Transactions on Medical Imaging.

[14]  Toshinori Hirai,et al.  Pseudostenosis phenomenon at volume-rendered three-dimensional digital angiography of intracranial arteries: frequency, location, and effect on image evaluation. , 2004, Radiology.

[15]  Alejandro F. Frangi,et al.  Efficient pipeline for image-based patient-specific analysis of cerebral aneurysm hemodynamics: technique and sensitivity , 2005, IEEE Transactions on Medical Imaging.

[16]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[17]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[18]  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.

[19]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[20]  R. Wasserman,et al.  Biomedical imaging modalities: a tutorial. , 1995, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[21]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[22]  Terry S. Yoo,et al.  Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis , 2004 .

[23]  G. Schroth,et al.  Contrast-enhanced 3D MR angiography of the carotid artery: comparison with conventional digital subtraction angiography. , 2002, AJNR. American journal of neuroradiology.

[24]  Laurence S. Dooley,et al.  Review of fuzzy image segmentation techniques , 2001 .

[25]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[26]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[27]  Michael Egmont-Petersen,et al.  Image processing with neural networks - a review , 2002, Pattern Recognit..

[28]  C. R. Ethier,et al.  Accuracy of Computational Hemodynamics in Complex Arterial Geometries Reconstructed from Magnetic Resonance Imaging , 2004, Annals of Biomedical Engineering.

[29]  W. Richardson.  Sobolev gradient preconditioning for image‐processing PDEs , 2006 .

[30]  Isaac N. Bankman,et al.  Handbook of Medical Imaging. Processing and Analysis , 2002 .

[31]  Kecheng Liu,et al.  Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review , 2002, IEEE Transactions on Information Technology in Biomedicine.

[32]  S Giordana,et al.  Automated classification of peripheral distal by-pass geometries reconstructed from medical data. , 2005, Journal of biomechanics.

[33]  Alejandro F. Frangi,et al.  Model-based quantitation of 3-D magnetic resonance angiographic images , 1999, IEEE Transactions on Medical Imaging.

[34]  Edward B. Diethrich,et al.  The clinical value of three-dimensional intravascular ultrasound imaging. , 1995 .

[35]  Kenneth M. Hanson,et al.  A framework for assessing uncertainties in simulation predictions , 1999 .

[36]  S Akselrod,et al.  Image segmentation in obstetrics and gynecology. , 2000, Ultrasound in medicine & biology.

[37]  Stephen R. Aylward,et al.  Analyzing attributes of vessel populations , 2005, Medical Image Anal..

[38]  S Giordana.  Geometrical reconstruction from medical images, classification and modelling of arterial by-pass grafts. , 2010 .

[39]  Alejandro F. Frangi,et al.  Model-based segmentation of cardiac and vascular images , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[40]  Dimitris N. Metaxas,et al.  A hybrid framework for 3D medical image segmentation , 2005, Medical Image Anal..

[41]  Joaquim Peiró,et al.  Reconstruction of shape and its effect on flow in arterial conduits , 2008 .

[42]  Michael Brady,et al.  3D Vascular Segmentation Using MRA Statistics and Velocity Field Information in PC-MRA , 2001, IPMI.

[43]  A. Kelemen,et al.  Three-dimensional model-based segmentation of brain MRI , 1998, Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162).

[44]  King-Sun Fu,et al.  A survey on image segmentation , 1981, Pattern Recognit..

[45]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[46]  Leon Axel,et al.  Tagged Magnetic Resonance Imaging of the Heart: a Survey , 2004 .