Optimized Bayesian NL-means Blockwise approach for Ultra Sound Images

 
 
 
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  • Abstract


    Ultrasound imaging is a portable medical diagnostic tool provides real time clear images of soft tissues compared to x-ray imaging in which tissues are not visible up to the mark. Other diagnostic tools like Magnetic Resonance Imaging and Computer Tomography can well visualize the tissues but are cost effective.  Due to the presence of speckle noise Ultrasound images gets degraded leads to low quality imaging. This noise down the spatial, contrast resolutions and Peak Signal to Noise Ratio (PSNR) in US images. Consequently, filtering techniques for speckle noise reduction are of unique interest for clinical ultrasound imaging.


  • References


      [1] Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE TIP, vol. 11, no. 11, pp. 1260–1270, 2002.

      [2] T. Loupas et al., “An adaptative weighted median filter for speckle suppression in medical ultrasound image,” IEEE T. Circ. Syst., vol. 36, pp. 129–135, 1989.

      [3] C. Kervrann et al., “Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal,” in SSVM’ 07, 2007, pp. 520–532.

      [4] K. Krissian et al., “Speckle-constrained anisotropic diffusion for ultrasound images,” in CVPR ’05, 2005, pp. 547–552.

      [5] M. Karaman, M. A. Kutay, and G. Bozdagi, “An adaptive speckle suppression filter for medical ultrasonic imaging,” IEEE Transactions on Medical Imaging, vol. 14, no. 2, pp. 283–292, 1995.

      [6] X. Zong, A. F. Laine, and E. A. Geiser, “Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing,” IEEE Transactions on Medical Imaging, vol. 17, no. 4, pp. 532–540, 1998.

      [7] N.Gupta, M.N.S. Swamy, E.Plotkin, “Despeckling of medical ultrasound images using data and rate adaptive lossy compression”, IEEE Trans. Med. Ima. 24(6)(2005) 743-754.

      [8] J.S. Lee, “Digital image enhancement and noise filtering by use of local statistics”, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-2 (1980) 165-168.

      [9] J. Tian and L. Chen,“Image despeckling using a nonparametric statistical model of wavelet coefficients”, Biomedical Signal Processing and Control 6 (2011) 432-437

      [10] L. I. Rudin, S. Osher and E. Faterni, “Nonlinear total variation based noise removal algorithms,” Physica D, vol. 60, pp. 259-268, 1992.

      [11] R. C. Gonzalez and R. E Woods., Digital Image Processing, Pearson Education, Second Edition, 2005

      [12] D.T Kuan et al., “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE PAMI, vol. 7, no. 2, pp. 165–177, 1985.

      [13] G. Slabaugh et al., “Ultrasound-specific segmentation via decorrelation and statistical region-based active contours,” in CVPR ’06, 2006, vol. 1, pp. 45–53.

      [14] M. S. Jensen et al., “A method to obtain reference images for evaluation of ultrasonic tissue characterization techniques,” Ultrasonics, vol. 40, no. 1-8, pp. 89–94, 2002.

      [15] Gopatoti, A., Naik, M.C., Gopathoti, K.K.” Convolutional Neural Network based image denoising for better quality of images”, International Journal of Engineering and Technology(UAE), Vol.7, No.3.27, (2018), pp. 356-361.

      [16] Gopatoti, A., Ramadass, N. “Performance of adaptive subband thresholding technique in image denoising”, Journal of Advanced Research in Dynamical and Control Systems, Vol. 9, No. 12, (2017), pp.151-157.


 

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Article ID: 22890
 
DOI: 10.14419/ijet.v7i4.16.22890




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