A study on CT aorta segmentation using vessel enhancement diffusion filter and region growing

Medical imaging in clinical diagnoses and treatment has been increasingly important, and CT scan has been applied more widely. It is important to extract blood vessels structure for improving planning and navigating in interventional procedures. However, the complexity of vascular structures and the limitation of angiography equipment make vessel segmentation from CT images more challenging. A method for extracting abdominal aorta from CT images is proposed based on vessel enhancing diffusion filters and three-dimensional region growing algorithm. In order to certify the proposed method, a group of clinical CT image series is used and the results show that the proposed method is an effective way for improving the accuracy of abdominal aorta segmentation.

[1]  Bart M. ter Haar Romeny,et al.  A Novel 3D Multi-scale Lineness Filter for Vessel Detection , 2007, MICCAI.

[2]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Horst Bischof,et al.  A Novel Approach for Detection of Tubular Objects and Its Application to Medical Image Analysis , 2008, DAGM-Symposium.

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

[5]  Benoit M. Macq,et al.  Segmentation using a region-growing thresholding , 2005, IS&T/SPIE Electronic Imaging.

[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]  K. Sum.  Vessel Extraction Under Non-Uniform Illumination : A Level Set Approach , 2009 .

[8]  Max A. Viergever,et al.  Vessel enhancing diffusion: A scale space representation of vessel structures , 2006, Medical Image Anal..

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

[10]  Jürgen Weese,et al.  Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images , 1997, CVRMed.

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

[12]  Zhengang Jiang,et al.  An Improved Image Segmentation Method Using Three-dimensional Region Growing Algorithm , 2013, ISCA 2013.

[13]  Karl Rohr,et al.  Segmentation and Quantification of Human Vessels Using a 3-D Cylindrical Intensity Model , 2007, IEEE Transactions on Image Processing.

[14]  Wiro J. Niessen,et al.  Multiscale Vessel Enhancing Diffusion in CT Angiography Noise Filtering , 2005, IPMI.

[15]  Isabelle Bloch,et al.  A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes , 2009, Medical Image Anal..

[16]  K.W. Sum,et al.  A Novel Active Contour Model Using Local and Global Statistics for Vessel Extraction , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.