Tracking the Left Ventricle in Ultrasound Images Based on Total Variation Denoising

Tracking the Left Ventricle (LV) in ultrasound sequences remains a challenge due to speckle noise, low SNR and lack of contrast. Therefore, it is usually difficult to obtain accurate estimates of the LV cavities since feature detectors produce a large number of outliers. This paper presents an algorithm which combines two main operations: i) a novel denoising algorithm based on the Lyapounov equation and ii) a robust tracker, based on an outlier feature model. Experimental results are provided, showing that the proposed algorithm is computationally efficient and leads to accurate estimates of the LV.

[1]  Terry M. Peters,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003 , 2003, Lecture Notes in Computer Science.

[2]  Nikos Paragios,et al.  Establishing Local Correspondences towards Compact Representations of Anatomical Structures , 2003, MICCAI.

[3]  A. Bidani,et al.  Ultrasonic speckle suppression using robust nonlinear wavelet diffusion for LV volume quantification , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Stanley Osher,et al.  Level Set Methods , 2003 .

[5]  S. Osher,et al.  Geometric Level Set Methods in Imaging, Vision, and Graphics , 2011, Springer New York.

[6]  Michael Isard,et al.  Active Contours , 2000, Springer London.

[7]  Y. Bar-Shalom Tracking and data association , 1988 .

[8]  Andrew Hammoude,et al.  Computer-assisted endocardial border identification from a sequence of two-dimensional echocardiographic images , 1988 .

[9]  Johan Montagnat,et al.  Anisotropic filtering for model-based segmentation of 4D cylindrical echocardiographic images , 2003, Pattern Recognit. Lett..

[10]  Aleksandra Pizurica,et al.  Wavelet based denoising techniques for ultrasound images , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[11]  José M. Bioucas-Dias,et al.  Fast GEM wavelet-based image deconvolution algorithm , 2003, ICIP.

[12]  Ian G. Cumming,et al.  Bayesian speckle noise reduction using the discrete wavelet transform , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[13]  Jorge S. Marques,et al.  Robust shape tracking in the presence of cluttered background , 2000, IEEE Transactions on Multimedia.

[14]  João M. Sanches,et al.  Image Denoising Using the Lyapunov Equation from Non-uniform Samples , 2006, ICIAR.

[15]  Torbjørn Eltoft,et al.  Modeling the amplitude statistics of ultrasonic images , 2006, IEEE Transactions on Medical Imaging.

[16]  Scott T. Acton,et al.  Edge detection in ultrasound imagery using the instantaneous coefficient of variation , 2004, IEEE Transactions on Image Processing.

[17]  S. Gupta,et al.  Wavelet-based statistical approach for speckle reduction in medical ultrasound images , 2003, Medical and Biological Engineering and Computing.

[18]  Mohamed S. Kamel,et al.  Image Analysis and Recognition , 2014, Lecture Notes in Computer Science.

[19]  C. Burckhardt Speckle in ultrasound B-mode scans , 1978, IEEE Transactions on Sonics and Ultrasonics.

[20]  T. Loupas,et al.  An adaptive weighted median filter for speckle suppression in medical ultrasonic images , 1989 .