Automatic Centerline Extraction of Irregular Tubular Structures Using Probability Volumes from Multiphoton Imaging

In this paper, we present a general framework for extracting 3D centerlines from volumetric datasets. Unlike the majority of previous approaches, we do not require a prior segmentation of the volume nor we do assume any particular tubular shape. Centerline extraction is performed using a morphology-guided level set model. Our approach consists of: i) learning the structural patterns of a tubular-like object, and ii) estimating the centerline of a tubular object as the path with minimal cost with respect to outward flux in gray level images. Such shortest path is found by solving the Eikonal equation. We compare the performance of our method with existing approaches in synthetic, CT, and multiphoton 3D images, obtaining substantial improvements, especially in the case of irregular tubular objects.

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

[2]  Anthony J. Yezzi,et al.  Vessel Segmentation Using a Shape Driven Flow , 2004, MICCAI.

[3]  Ioannis A. Kakadiaris,et al.  Adaptive Frames-Based Denoising of Confocal Microscopy Data , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[4]  Laurent D. Cohen,et al.  Fast extraction of tubular and tree 3D surfaces with front propagation methods , 2002, Object recognition supported by user interaction for service robots.

[5]  Kaleem Siddiqi,et al.  Flux driven automatic centerline extraction , 2005, Medical Image Anal..

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

[7]  Antti Oulasvirta,et al.  Computer Vision – ECCV 2006 , 2006, Lecture Notes in Computer Science.

[8]  Ron Kimmel,et al.  Fast Edge Integration , 2003 .

[9]  Aly A. Farag,et al.  Differential Fly-Throughs (DFT): A General Framework for Computing Flight Paths , 2005, MICCAI.

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

[11]  Guido Gerig,et al.  3D Multi-scale line filter for segmentation and visualization of curvilinear structures in medical images , 1997, CVRMed.

[12]  I. Kakadiaris,et al.  Towards Segmentation of Irregular Tubular Structures in 3 D Confocal Microscope Images , 2006 .

[13]  Ioannis A. Kakadiaris,et al.  Automatic Reconstruction of Dendrite Morphology from Optical Section Stacks , 2006, CVAMIA.

[14]  Nikos Paragios,et al.  Globally Optimal Active Contours, Sequential Monte Carlo and On-Line Learning for Vessel Segmentation , 2006, ECCV.

[15]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[16]  Kaleem Siddiqi,et al.  Flux maximizing geometric flows , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.