A High-Order Multiscale Features Incorporated Bayesian Method for Cerebrovascular Segmentaiton from TOF MRA

This paper presents a supervised statistical-based cerebrovascular segmentation method from time-of-flight MRA. The novelty of this method is that rather than model the dataset over the entire intensity range, we at first use a low threshold to eliminate the lowest intensity region, and then use two uniform distributions to model the middle and high intensity regions, respectively. Subsequently, in order to overcome the intensity overlap between subcutaneous fat and arteries, a high order multiscale features based energy function is introduced to enhance the segmentation. Comparing with those sole intensity based segmentation method the newly proposed algorithm can solve the problem of the regional intensity variation of TOF-MRA well and improve the quality of segmentation. The experimental results also show that the proposed method can provide a better quality segmentation than sole intensity information used method.

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

[2]  Kecheng Liu,et al.  A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II , 2002, IEEE Transactions on Information Technology in Biomedicine.

[3]  Alejandro F Frangi,et al.  Non-parametric geodesic active regions: Method and evaluation for cerebral aneurysms segmentation in 3DRA and CTA , 2007, Medical Image Anal..

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

[5]  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.

[6]  Jürgen Weese,et al.  A Multi-scale Line Filter with Automatic Scale Selection Based on the Hessian Matrix for Medical Image Segmentation , 1997, Scale-Space.

[7]  J. Alison Noble,et al.  Statistical 3D Vessel Segmentation Using a Rician Distribution , 1999, MICCAI.

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

[9]  Francis K. H. Quek,et al.  A review of vessel extraction techniques and algorithms , 2004, CSUR.

[10]  J. Alison Noble,et al.  Fusing speed and phase information for vascular segmentation of phase contrast MR angiograms , 2002, Medical Image Anal..

[11]  Aly A. Farag,et al.  Cerebrovascular segmentation from TOF using stochastic models , 2006, Medical Image Anal..

[12]  Aly A. Farag,et al.  Automatic Cerebrovascular Segmentation by Accurate Probabilistic Modeling of TOF-MRA Images , 2005, MICCAI.

[13]  Max A. Viergever,et al.  Fast delineation and visualization of vessels in 3-D angiographic images , 2000, IEEE Transactions on Medical Imaging.

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

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

[16]  Olivier D. Faugeras,et al.  CURVES: Curve evolution for vessel segmentation , 2001, Medical Image Anal..

[17]  Ashraf A. Kassim,et al.  Segmentation of volumetric MRA images by using capillary active contour , 2006, Medical Image Anal..