Characteristics measurement for blood vessels diseases detection based on cone-beam CT images

This paper describes characteristics measurement for blood vessels diseases detection based on high-resolution 3D blood vessel images obtained by cone-beam CT. The important objective here is to show how the representation of blood vessel morphology can lead to feature measurement and identification of abnormal regions. In our method, the blood vessel structures are extracted by a graph description of blood vessel centerlines and surfaces representation using curvatures. The measurements of the anatomical information, such as the blood vessel's orientation, cross-sectional area, surface shapes, and abnormal regions volumes, are based on the structure description. In order to alert the doctor's attention to the location of the blood vessel's abnormality, we attempt to enhance the difference between malformed and normal shapes using the measured blood vessel characteristics. In this paper, we focus on a method based on surfaces representation and we present examples of cone-beam CT images of a patient's blood vessels.

[1]  Marc Levoy,et al.  Display of surfaces from volume data , 1988, IEEE Computer Graphics and Applications.

[2]  Noboru Niki,et al.  Three-dimensional imaging of blood vessels using cone-beam CT , 1994, Proceedings of 1st International Conference on Image Processing.

[3]  Noboru Niki,et al.  A 3-D display method of fuzzy shapes obtained from medical images , 1991, Systems and Computers in Japan.

[4]  Yves L. Trousset,et al.  Multiscale cone-beam x-ray reconstruction , 1990, Medical Imaging.

[5]  Andrea J. van Doorn,et al.  Surface shape and curvature scales , 1992, Image Vis. Comput..

[6]  Guido Gerig,et al.  Surface parametrization and shape description , 1992, Other Conferences.

[7]  Noboru Niki,et al.  Three-dimensional image analysis of blood vessels using cone-beam CT , 1994, Proceedings of 1994 IEEE Nuclear Science Symposium - NSS'94.

[8]  J. Sklansky,et al.  Estimating the 3D skeletons and transverse areas of coronary arteries from biplane angiograms. , 1988, IEEE transactions on medical imaging.

[9]  Ramesh C. Jain,et al.  Invariant surface characteristics for 3D object recognition in range images , 1985, Comput. Vis. Graph. Image Process..

[10]  J. Fessler,et al.  Object-based 3-D reconstruction of arterial trees from magnetic resonance angiograms. , 1991, IEEE transactions on medical imaging.

[11]  Guido Gerig,et al.  Towards representation of 3D shape: global surface parametrization , 1992 .

[12]  M. Giger,et al.  Digital Radiography , 1993, Acta radiologica.

[13]  Steven W. Zucker,et al.  Inferring Surface Trace and Differential Structure from 3-D Images , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Norishige Chiba,et al.  A speedy voxel-tracing method for cutting solid display of 3-D images , 1992, Systems and Computers in Japan.

[15]  M. Levoy Volume Rendering Display of Surfaces from Volume Data , 1988 .

[16]  Ramesh Jain,et al.  Symbolic Surface Descriptors For 3-Dimensional Object Recognition , 1987, Photonics West - Lasers and Applications in Science and Engineering.

[17]  Yoshiki Kawata,et al.  Three-dimensional blood vessel reconstruction using a high-speed X-ray rotational projection system , 1994, Systems and Computers in Japan.

[18]  Richard A. Robb,et al.  The Dynamic Spatial Reconstructor: An X-Ray Video-Fluoroscopic CT Scanner for Dynamic Volume Imaging of Moving Organs , 1982, IEEE Transactions on Medical Imaging.

[19]  Jae S. Lim,et al.  A new method for estimation of coronary artery dimensions in angiograms , 1988, IEEE Trans. Acoust. Speech Signal Process..

[20]  Frank P. Ferrie,et al.  Darboux Frames, Snakes, and Super-Quadrics: Geometry from the Bottom Up , 1993, IEEE Trans. Pattern Anal. Mach. Intell..