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Tytuł artykułu

Models, algorithms and applications in vascular image segmentation

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A synthesis of the authors' projects in the field of 3D vascular image processing in the last decadeis provided. This work was motivated by the following applications: display improvement, extraction of geometrical measurements, acquisition optimization, stent-pose planning, phantom generation, blood-flow simulations. The methods are often dependent on the imaging modality and/or on the anatomic region. They involve both: low-level models of intensity patterns and profiles, and higher-level models of cylindrical shapes. Amongst the various algorithms used, recursive tracking and fast-marching level-sets are emphasized. Critical analysis of each model and algorithm is carried out. Problems that remain open, and perspectives associated with the progress of the image acquisition techniques, are listed.
Rocznik
Strony
5--33
Opis fizyczny
Bibliogr. 38 poz., rys., tab., wykr.
Twórcy
autor
autor
  • CREATIS-LRMN, Universite Lyon 1, INSA-Lyon, CNRS (UMR 5220) and INSERM (U630) Research Unit, France
Bibliografia
  • [1] Kass M., Witkin A., Terzopoulos D.: Snakes: Active contour models. Int. J. Comput. Vision 1 (4), 321-331, 1987.
  • [2] Cohen L. D.: On active contour models and balloons. Comp. Vision Grapgh. Image Process: Image Underest, 53 (2), 211-218, 1991.
  • [3] Staib L. H. and Duncan J. S.: Boundary finding with parametrically deformable models. IEEE Pattern Anal. Mach. Intell., 14, 1061-1075, 1992.
  • [4] Du Y. P., Parker D. L., Davis W. L.: Vessel enhancement filtering in three-dimensional MR angiography. J. Magn. Reson. Imaging, 5, 151-157, 1995.
  • [5] Verdonck B., Bloch I. and Maitre H.: Accurate segmentation of blood vessels from 3D medical images. In: Int. Conf. Image Proc., Lausanne, Switzerland, 3, 311-314, 1996.
  • [6] Orkisz M., Bresson C., Magnin I. E., Champin O., Douek P. C.: Improved vessel visualization in MR Angiography by non-linear anisotropic filtering. Magn. Reson. Med., 37, 914-919, 1997.
  • [7] Swift R. D., Ramaswamy K. and Higgins W. E.: Adaptive axes generation algorithm for 3D tubular structures. In: Int. Conf. Image. Proc., Sta Barbara, CA, USA, 2, 136-139, 1997.
  • [8] Sato Y., Nakajima S., Shiraga N., Atsumi H., Yoshida S., Koller T., Gerig G. and Kikinis R.: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med. Image. Anal., 2 (2), 143-168, 1998.
  • [9] Frangi A. F., Niessen W. J., Hoogeveen R.M., Walsum T. and Viergever M.A.: Model-based quantitationo of 3-D magnetic resonance angiographic images. IEEE Trans. Med. Imaging, 18 (10), 946-956, 1999.
  • [10] Sethian J. A.: Level set methods and fast marching methods. Evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science, Cambridge University Press, Cambridge, 1999.
  • [11] Delingette, H.: General Object Reconstruction based on Simplex Meshes. Int. J. Comput. Vision, 32 (2), 111-146, 1999.
  • [12] Orkisz M., Hernandez Hoyos M., Douek P. C., Magnin I. E.: Advances of blood vessel morphology analysis in 3D magnetic resonance images. Mach.Graph. & Vision, 2000, 9 (1/2), 463-471.
  • [13] Wink O., Niessen W. J. and Viergever M. A.: Fast delineation and visualization of vessel in 3D angiography images. IEEE Trans. Med. Imaging, 19 (4), 337-346, 2000.
  • [14] Orkisz M., Hernandez Hoyos M.: Models for 3D vascular image analysis. J. Med. Informatics Technol. 2 (1), IP13-IP22, 2001.
  • [15] Antiga L., Ene-Iordache B., Remuzzi G., Remuzzi A.: Automatic generation of glomerular capillary topological organization. Microvascular Research 62, 346-354, 2001.
  • [16] Flasque N., Desvignes M., Constans J. M., Revenu M.: Acquisition, segmentation and tracking of the cerebral vascular tree on 3D magnetic resonance angiography images. Med. Image. Anal. 5, 173-183, 2001.
  • [17] Toumoulin C., Boldak C., Dillenseger J. L., Coatrieux J. L., Rolland Y.: Fast detection and characterization of vessels in very large data sets using geometrical moments. IEEE Trans. Biomed. Eng. 48, 604-606, 2001.
  • [18] Suri J. S., Liu K., Reden L., Laxminarayan S.: A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II. IEEE Trans Information Technol Biomed 6, 338-350, 2002.
  • [19] Hernandez Hoyos M., Orkisz M., Puech P., Mansard-Desbleds C., Douek P. C., Magnin I. E.: Computer assisted analysis of 3D MRA images. RadioGraphics, 22 (2), 421-436, 2002.
  • [20] Wink O., Frangi A. F., Verdonck B., Viergever M. A., Niessen W. J.: 3D MRA coronary axis deter mination using a minimum cost path approach. Magn. Reson. Med., 47, 1169-1175, 2002.
  • [21] Azencot J., Orkisz M.: Deterministic and stochastic state model of right generalized cylinder (RGC-I sm): application in computer phantoms synthesis, Graph. Mod., 65, 323-350, 2003.
  • [22] Boldak C., Rolland Y., Toumoulin C., Coatrieux J. L.: An improved model-based vessel tracking algorithm with application to Computed Tomography Angiography. J. Biocybern Biomed Eng., 3 (1), 41-64, 2003.
  • [23] Kirbas C., Quek F.K.H.: A review of vessel extraction techniąues and algorithms. ACM Comput Surv 36, 81-121, 2004.
  • [24] Desbleds Mansard C., Canet Soulas E. P., Anwander A., Chaabane L., Neyran B., Serfaty J.-M., Magnin I. E., Douek P. C., Orkisz M.: Quantification of multi-contrast yascular MR Images with the NLSnake, an active contour model: in vitro validation and in vivo evaluation, Magn. Reson. Med., 51 (2), 370-379, 2004.
  • [25] Florez Valencia L., Montagnat J., Orkisz M.: 3D graphical models for vascular-stent pose simulation, Mach. Graph. Vision., 13.(3), 235-248, 2004.
  • [26] Florez Valencia L., Vincent F., Orkisz M.: Fast 3D pre-segmentation of arteries in computed tomography angiograms. In: Int. Conf.Comput. Vision Graph., Warsaw, Poland, Springer Verlag, 361-366, 2004.
  • [27] Carrillo J. F., Orkisz M., Hernandez Hoyos M.: Extraction of 3D vascular tree skeletons based on the analysis of connected components evolution. In: CAIP'2005 - 11th Int IAPR Conf. Computer Analysis of Images and Patterns, Yersailles, France, Springer Verlag LNCS 3691, 604-611, 2005.
  • [28] Hernandez Hoyos M., Orkisz M., Douek P. C., Magnin I. E.: Assessment of carotid artery stenoses in 3D contrast-enhanced magnetic resonance angiography, based on improyed generation of the centerline. Mach. Graphics Vision 14 (4), 349-378, 2005.
  • [29] Hernandez Hoyos M., Orłowski P., Piatkowska-Janko E., Bogorodzki P., Orkisz M.: Vascular centerline extraction in 3D MR angiograms for phase contrast MRI blood flow measurement, Int J. Comp. Assisted Radiol Surg., 1 (1), 51-61 (DOI: 10.1007/sll548-006-0005-0), 2005.
  • [30] Lorenz C., von Berg J.: Fast automated object detection by recursiye casting of search rays". In: Comp. Assisted Radiol Surg., Berlin, Elsevier-Verlag, 230-235, 2005.
  • [31] Leli M. M., Anders K., Uder M., Klotz E., Ditt H., Vega-Higuera F., Boskamp T., Bautz W. A., Tomandl B. F.: New techniques in CT angiography. Radiographics, 26, S45-S62. 2006.
  • [32] Hernandez Hoyos M., Serfaty J. M., Maghiar A., Mansard C., Orkisz M., Magnin I. E., Douek P. C.: Evaluation of semi-automatic arterial stenosis quantification. Int J. Comp. Assisted Radiol Surg., 1 (3),167-175 (DOI 10.1007/sll548-006-0049-l), 2006.
  • [33] Florez Valencia L., Azencot J., Vincent F., Orkisz M., Magnin I. E.: Segmentation and quantification of blood vessels in 3d images using a right generalized cylinder state model, Int Conf. Image Process, Atlanta, GA, USA, 2441-2444, 2006.
  • [34] Carrillo J. F., Hernandez Hoyos M., Dayila E. E., Orkisz M.: Recursive tracking of vascular tree axes in 3D medical images, Int J. Comp. Assisted Radiol Surg., 1 (6), 331-339 (DOI 10.1007/sl 1548-007-0068-6), 2007.
  • [35] Zuluaga Valencia M. A., Davila E. E., Uriza L. F., Hernandez Hoyos M.: Carotid artery segmentation and characterization in 3D Computed Tomography (CT) images. In: Comp. Assisted Radiol Surg., Berlin, Elsevier-Verlag, accepted., 2007.
  • [36] Florez Valencia L., Baltaxe Milwer M., Hernandez Hoyos M., Vincent F., Douek P. C., Magnin I. E., Orkisz M.: Fast marching level-sets for the extraction of yascular cross-sectional contours in CT angiography images. In: Comp Assisted Radiol Surg, Berlin, Elsevier-Verlag, accepted, 2007.
  • [37] Florez Valencia L., Montagnat J., Orkisz M.: 3D models for vascular lumen segmentation in MRA images and for artery-stenting simulation, ITBM-RBM Innov. Technol. Biol. Med., accepted (DOI 10.1016/j.rbmret.2007.04.001), 2007.
  • [38] Baltaxe Milwer M., Florez Valencia L., Hernandez Hoyos M., Magnin I. E., Orkisz M.: Fast-marching contours for the segmentation of vessel lumen in CTA cross-sections. In: EMBC - 29th Annual Int Conf.IEEE Eng. Med. Biol. Soc., Lyon, France, accepted, 2007.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA1-0032-0001
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