Automatically Driven Vector for Guidewire Segmentation in 2D and Biplane Fluoroscopy
The segmentation of endovascular tools in fluoroscopy images can be accurately performed automatically or by minimum user intervention, using known modern techniques. It has been proven in literature, but no clinical implementation exists so far because the computational time requirements of such technology have not yet been met. A classical segmentation scheme is composed of edge enhancement filtering, line detection, and segmentation. A new method is presented that consists of a vector that propagates in the image to track an edge as it advances. The filtering is performed progressively in the projected path of the vector, whose orientation allows for oriented edge detection, and a minimal image area is globally filtered. Such an algorithm is rapidly computed and can be implemented in real-time applications. It was tested on medical fluoroscopy images from an endovascular cerebral intervention. Ex- periments showed that the 2D tracking was limited to guidewires without intersection crosspoints, while the 3D implementation was able to cope with such planar difficulties.
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algorithm for edge enhancement and detection," Image Analysis and
Recognition, vol. 3211, pp. 187-194, 2004.
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"Detection of electrophysiology catheters in noisy fluoroscopy images,"
in MICCAI, 2006, pp. 25-32.
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tree models," in 28th annual workshop of the Austrian Association for
Pattern Recognition, 1999.
[11] D. Palti-Wasserman, A. M. Bruckstein, and R. P. Beyar, "Identifying and
tracking a guide wire in the coronary arteries during angioplasty from
x-ray images," IEEE Transactions on Biomedical Engineering, vol. 44,
no. 2, pp. 152-164, Feb 1997.
[12] S. A. M. Baert, E. B. van de Kraats, T. van Walsum, M. A. Viergever,
and W. J. Niessen, "Three-dimensional guide-wire reconstruction from
biplane image sequences for integrated display in 3-d vasculature," IEEE
Transaction on Medical Imaging, vol. 22, no. 10, pp. 1252-1258, Oct
2003.
[13] T. van Walsum, S. A. M. Baert, and W. J. Niessen, "3d guide wire
visualization in 3dra using monoplane fluoroscopic imaging," in SPIE
Medical Imaging, vol. 5029, 2003, pp. 166-175.
[14] M. Zarkh and M. Klaiman, "Guide wire navigation and therapeutic
device localization for catheterization procedure," in Computer Assisted
Radiology and Surgery, vol. 1281, May 2005, pp. 311-316.
[15] A. Barbu, V. Athitsos, B. Georgescu, S. Boehm, P. Durlak, and D. Co-
maniciu, "Hierarchical learning of curves application to guidewire local-
ization in fluoroscopy," IEEE Computer Vision and Pattern Recognition,
pp. 1-8, June 2007.
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Krieger Publishing Company, June 1992.
[1] L. Guimar¨aes, A. Soares, V. Cordeiro, and A. Susin, "Gradient pile up
algorithm for edge enhancement and detection," Image Analysis and
Recognition, vol. 3211, pp. 187-194, 2004.
[2] E. Franken, P. Rongen, M. van Almsick, and B. M. ter Haar Romeny,
"Detection of electrophysiology catheters in noisy fluoroscopy images,"
in MICCAI, 2006, pp. 25-32.
[3] A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever,
"Multiscale vessel enhancement filtering," in Medical Image Computing
and Computer-Assisted Intervention, ser. Lecture Notes in Computer
Science, vol. 1496, 1998, pp. 130-137.
[4] J. George and S. Indu, "Fast adaptive anisotropic filtering for medical
image enhancement," Dec. 2008, pp. 227-232.
[5] K. Lai and R. Chin, "Deformable contours: modeling and extraction,"
Pattern Analysis and Machine Intelligence, IEEE Transactions on,
vol. 17, no. 11, pp. 1084-1090, Nov 1995.
[6] M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active
contour models," International Journal of Computer Vision,
vol. V1, no. 4, pp. 321-331, January 1988. (Online). Available:
http://dx.doi.org/10.1007/BF00133570
[7] C. Xu and J. Prince, "Snakes, shapes, and gradient vector flow," Image
Processing, IEEE Transactions on, vol. 7, no. 3, pp. 359-369, Mar 1998.
[8] I. Ben Ayed and A. Mitiche, "A region merging prior for variational
level set image segmentation," IEEE Transactions on Image Processing,
vol. 17, no. 12, pp. 2301-2311, Dec 2008.
[9] P.-L. Bazin and D. L. Pham, "Topology correction of segmented medical
images using a fast marching algorithm," Comput. Methods Prog.
Biomed., vol. 88, no. 2, pp. 182-190, 2007.
[10] G. Langs, P. Radeva, and F. Carreras, "Explorative building of 3d vessel
tree models," in 28th annual workshop of the Austrian Association for
Pattern Recognition, 1999.
[11] D. Palti-Wasserman, A. M. Bruckstein, and R. P. Beyar, "Identifying and
tracking a guide wire in the coronary arteries during angioplasty from
x-ray images," IEEE Transactions on Biomedical Engineering, vol. 44,
no. 2, pp. 152-164, Feb 1997.
[12] S. A. M. Baert, E. B. van de Kraats, T. van Walsum, M. A. Viergever,
and W. J. Niessen, "Three-dimensional guide-wire reconstruction from
biplane image sequences for integrated display in 3-d vasculature," IEEE
Transaction on Medical Imaging, vol. 22, no. 10, pp. 1252-1258, Oct
2003.
[13] T. van Walsum, S. A. M. Baert, and W. J. Niessen, "3d guide wire
visualization in 3dra using monoplane fluoroscopic imaging," in SPIE
Medical Imaging, vol. 5029, 2003, pp. 166-175.
[14] M. Zarkh and M. Klaiman, "Guide wire navigation and therapeutic
device localization for catheterization procedure," in Computer Assisted
Radiology and Surgery, vol. 1281, May 2005, pp. 311-316.
[15] A. Barbu, V. Athitsos, B. Georgescu, S. Boehm, P. Durlak, and D. Co-
maniciu, "Hierarchical learning of curves application to guidewire local-
ization in fluoroscopy," IEEE Computer Vision and Pattern Recognition,
pp. 1-8, June 2007.
[16] S. R. Deans, The Radon Transform and Some of Its Applications.
Krieger Publishing Company, June 1992.
@article{"International Journal of Medical, Medicine and Health Sciences:54094", author = "Simon Lessard and Pascal Bigras and Caroline Lau and Daniel Roy and Gilles Soulez and Jacques A. de Guise", title = "Automatically Driven Vector for Guidewire Segmentation in 2D and Biplane Fluoroscopy", abstract = "The segmentation of endovascular tools in fluoroscopy images can be accurately performed automatically or by minimum user intervention, using known modern techniques. It has been proven in literature, but no clinical implementation exists so far because the computational time requirements of such technology have not yet been met. A classical segmentation scheme is composed of edge enhancement filtering, line detection, and segmentation. A new method is presented that consists of a vector that propagates in the image to track an edge as it advances. The filtering is performed progressively in the projected path of the vector, whose orientation allows for oriented edge detection, and a minimal image area is globally filtered. Such an algorithm is rapidly computed and can be implemented in real-time applications. It was tested on medical fluoroscopy images from an endovascular cerebral intervention. Ex- periments showed that the 2D tracking was limited to guidewires without intersection crosspoints, while the 3D implementation was able to cope with such planar difficulties.
", keywords = "Edge detection, Line Enhancement, Segmentation,Fluoroscopy.", volume = "3", number = "6", pages = "63-5", }
