Snake modeling and distance transform approach to vascular centerline extraction and quantification.

A new method for fully automated centerline extraction and quantification of microvascular structures in confocal microscopy (CM) images is presented. Our method uses the idea of active contour models as well as path planning and distance transforms for the three-dimensional centerline extraction of elongated objects such as vessels. The proposed approach is especially efficient for centerline extraction of complex branching structures. The method performance is validated in several CM images of both normal and stroked rat brains as well as simulated objects. The results confirm the efficiency of the proposed method in extracting the medial curve of vessels, which is essential for the computation of quantitative parameters.

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

[2]  Federico Thomas,et al.  Fast skeletonization of spatially encoded objects , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[3]  O. Cuisenaire.  Distance transformations: fast algorithms and applications to medical image processing , 1999 .

[4]  J. Alison Noble,et al.  Segmentation of Cerebral Vessels and Aneurysms from MR Angiography Data , 1997, IPMI.

[5]  Jen-Hui Chuang,et al.  Skeletonization of Three-Dimensional Object Using Generalized Potential Field , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

[7]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

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

[9]  Arthur W. Toga,et al.  Efficient Skeletonization of Volumetric Objects , 1999, IEEE Trans. Vis. Comput. Graph..

[10]  Milan Sonka,et al.  A Fully Parallel 3D Thinning Algorithm and Its Applications , 1996, Comput. Vis. Image Underst..

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

[12]  Yaorong Ge,et al.  On the Generation of Skeletons from Discrete Euclidean Distance Maps , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Kálmán Palágyi,et al.  A 3-subiteration 3D thinning algorithm for extracting medial surfaces , 2002, Pattern Recognit. Lett..

[14]  Ali Shahrokni,et al.  Fast skeletonization algorithm for 3D elongated objects , 2001, SPIE Medical Imaging.

[15]  D S Paik,et al.  Automated flight path planning for virtual endoscopy. , 1998, Medical physics.

[16]  R. Brubaker.  Models for the perception of speech and visual form: Weiant Wathen-Dunn, ed.: Cambridge, Mass., The M.I.T. Press, I–X, 470 pages , 1968 .

[17]  Benjamin B. Kimia,et al.  Boundary Smoothing via Symmetry Transforms , 2001, Journal of Mathematical Imaging and Vision.

[18]  Hartmut Schirmacher.  Extracting Graphs from Three Dimensional Neuron Data Sets , 1998 .

[19]  Stina Svensson,et al.  Distance transforms in 3D using four different weights , 2002, Pattern Recognit. Lett..

[20]  Pierre Soille,et al.  Order independent homotopic thinning for binary and grey tone anchored skeletons , 2002, Pattern Recognit. Lett..

[21]  Gabriella Sanniti di Baja,et al.  Curve Skeletonization by Junction Detection in Surface Skeletons , 2001, IWVF.

[22]  Deborah Silver,et al.  Parameter-Controlled Volume Thinning , 1999, Graph. Model. Image Process..

[23]  David R. Stelts,et al.  Computing the centerline of a colon: a robust and efficient method based on 3D skeletons. , 1999, Journal of computer assisted tomography.

[24]  Laurent D. Cohen,et al.  Global Minimum for Active Contour Models: A Minimal Path Approach , 1997, International Journal of Computer Vision.

[25]  Ali Afzali-Kusha,et al.  Efficient center-line extraction for quantification of vessels in confocal microscopy images. , 2003, Medical physics.

[26]  Gabriella Sanniti di Baja,et al.  Computing skeletons in three dimensions , 1999, Pattern Recognit..

[27]  Bidyut Baran Chaudhuri,et al.  Skeletonization by a topology-adaptive self-organizing neural network , 2001, Pattern Recognit..

[28]  Ioannis Pitas,et al.  A generalized fuzzy mathematical morphology and its application in robust 2-D and 3-D object representation , 2000, IEEE Trans. Image Process..