Contour Extraction from IVUS Images Based on GVF Snakes and Wavelet Transform

The extraction of luminal borders (contours) from intravascular ultrasound (IVUS) images is helpful for the diagnosis of coronary artery diseases. A novel scheme for contour extraction is proposed in this paper, based on GVF snakes and wavelet transform. To solve the two difficulties of the traditional GVF snake, i.e. the contour initialization and the suppression of noise and artifact interference, there are two improvements in our proposed scheme. First, the procedure is made into full automation, with adopting the characteristics of the image sequences to produce an initial contour. Secondly, the robustness of the algorithm is enhanced, since the GVF snake is combined with discrete wavelet transform to deform the contour in multiscale images. The proposed scheme is verified on both the synthetic images and real IVUS images. The results show that this scheme is superior to traditional GVF snake in terms of boundary localization.

[1]  Johan H. C. Reiber,et al.  Automatic border detection in IntraVascular UltraSound images for quantitative measurements of the vessel, lumen and stent parameters , 2001, CARS.

[2]  Andrew H. Gee,et al.  Adaptive segmentation of ultrasound images , 1999, Image Vis. Comput..

[3]  Jouke Dijkstra,et al.  Automatic border detection in intravascular iltrasound images for quantitative measurements of the vessel, lumen and stent parameters , 2001, Computers in Cardiology 2001. Vol.28 (Cat. No.01CH37287).

[4]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Scott T. Acton,et al.  Vessel boundary tracking for intravital microscopy via multiscale gradient vector flow snakes , 2004, IEEE Transactions on Biomedical Engineering.

[6]  P. Yock,et al.  Intravascular ultrasound: novel pathophysiological insights and current clinical applications. , 2001, Circulation.

[7]  R.A. Zoroofi,et al.  Automatic extraction and measurement of leukocyte motion in microvessels using spatiotemporal image analysis , 1997, IEEE Transactions on Biomedical Engineering.

[8]  Fritz Albregtsen,et al.  Segmentation of ultrasound images of liver tumors applying snake algorithms and GVF , 2005 .

[9]  Scott T. Acton,et al.  Speckle reducing anisotropic diffusion , 2002, IEEE Trans. Image Process..

[10]  Tomy Varghese,et al.  Segmentation of elastographic images using a coarse-to-fine active contour model. , 2006, Ultrasound in medicine & biology.

[11]  Ruey-Feng Chang,et al.  3-D snake for US in margin evaluation for malignant breast tumor excision using mammotome , 2003, IEEE Transactions on Information Technology in Biomedicine.

[12]  Junaed Sattar.  Snakes , Shapes and Gradient Vector Flow , 2022 .

[13]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Frits Mastik,et al.  Fully automatic luminal contour segmentation in intracoronary ultrasound imaging-a statistical approach , 2004, IEEE Transactions on Medical Imaging.

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