Curvelet Based Image Fusion for Ultrasound Contrast Harmonic Imaging

Contrast harmonic imaging technology has been greatly developed in recent years for its better performance compared with tissue harmonic imaging. The unique imaging mode of contrast harmonic makes it possible to detect and predict the character of necrotic organs. More detailed information can be observed distinctly in tissue region when contrast harmonic imaging is employed, but the boundary is hard to distinguish since it is less sensitive than microbubble. Correspondingly, traditional ultrasound fundamental imaging mode can obtain clear boundary information while the internal information submerged in noises. A curvelet based image fusion method is proposed to obtain result images where both boundary and internal tissue information can be distinctly observed employing the fundamental image and the corresponding harmonic image. The experiments indicate that our proposed algorithm can include both kinds of important information for diagnosis in the result image than other fusion algorithms.

[1]  J. S. Kim,et al.  Ultrasonographic evaluation of the gallbladder: comparison of fundamental, tissue harmonic, and pulse inversion harmonic imaging. , 2001, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[2]  N de Jong,et al.  Ultrasound contrast imaging: current and new potential methods. , 2000, Ultrasound in medicine & biology.

[3]  Peng Jiang,et al.  A new tissue harmonic imaging scheme with better fundamental frequency cancellation and higher signal-to-noise ratio , 1998, 1998 IEEE Ultrasonics Symposium. Proceedings (Cat. No. 98CH36102).

[4]  Yun Zhang,et al.  Wavelet based image fusion techniques — An introduction, review and comparison , 2007 .

[5]  Pai-Chi Li,et al.  Harmonic leakage and image quality degradation in tissue harmonic imaging. , 2001, IEEE transactions on ultrasonics, ferroelectrics, and frequency control.

[6]  Chongzhao Han,et al.  An Overview on Pixel-Level Image Fusion in Remote Sensing , 2007, 2007 IEEE International Conference on Automation and Logistics.

[7]  Nico Karssemeijer,et al.  Ultrasonics, Ferroelectrics, and Frequency Control , 2011 .

[8]  Laurent Demanet,et al.  Fast Discrete Curvelet Transforms , 2006, Multiscale Model. Simul..

[9]  L. Ziegler,et al.  Harmonic ultrasound: a review. , 2002, Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association.

[10]  Yi Shen,et al.  A quantitative method for evaluating the performances of hyperspectral image fusion , 2003, IEEE Trans. Instrum. Meas..

[11]  Emmanuel J. Candès,et al.  The curvelet transform for image denoising , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[12]  E. Candès Harmonic Analysis of Neural Networks , 1999 .

[13]  E. Candès,et al.  Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges , 2000 .

[14]  M. Averkiou,et al.  Techniques for perfusion imaging with microbubble contrast agents , 2001, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.