Comparative Study of Different Denoising Filters for Speckle Noise Reduction in Ultrasonic B-Mode Images

Image denoising involves processing of the image data to produce a visually high quality image. The denoising algorithms may be classified into two categories, spatial filtering algorithms and transform domain based algorithms. In this paper a comparative study of different denoising filters for speckle noise reduction in ultrasonic b-mode images based on calculating the Peak Signal to Noise Ratio (PSNR) value as a metric is presented. The quantitative results of comparison are tabulated by calculating the PSNR of the output image. Index Terms ـــ Image enhancement, Ultrasonic scan, Speckle noise, Denoising filters

[1]  Richard G. Baraniuk,et al.  Analysis of wavelet-domain Wiener filters , 1998, Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis (Cat. No.98TH8380).

[2]  Sridha Sridharan,et al.  Ieee Transactions on Information Forensics and Security: Special Issue on Human Detection and Recognition 1 Multi-scale Representation for 3d Face Recognition , 2022 .

[3]  Alexandros Karargyris,et al.  Segmenting anatomy in chest x-rays for tuberculosis screening , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Nevine Jacob,et al.  Image Denoising In The Wavelet Domain Using Wiener Filtering , 2004 .

[5]  Vasily Strela,et al.  Denoising Via Block Wiener Filtering in Wavelet Domain , 2001 .

[6]  Patrick Hébert,et al.  Median Filtering in Constant Time , 2007, IEEE Transactions on Image Processing.

[7]  Peter Kovesi,et al.  Phase Preserving Denoising of Images , 1999 .

[8]  Lianping Chen,et al.  Effects of different Gabor filters parameters on image retrieval by texture , 2004, 10th International Multimedia Modelling Conference, 2004. Proceedings..

[9]  M. Oelze,et al.  An ultrasonic imaging speckle-suppression and contrast-enhancement technique by means of frequency compounding and coded excitation , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[10]  John W. Clark,et al.  Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images , 2006, IEEE Transactions on Medical Imaging.

[11]  Daan Huybrechs,et al.  Wavelets with applications in signal and image processing , 2006 .

[12]  E. Costa,et al.  Ultrasound speckle reduction using modified gabor filters , 2007, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[13]  S. Arumuga Perumal,et al.  Image De-noising using Discrete Wavelet transform , 2008 .

[14]  Dave Hale,et al.  Recursive Gaussian filters , 2006 .

[15]  Yuan Yan Tang,et al.  Wavelet Analysis and Its Applications , 2003 .

[16]  A. Tannenbaum,et al.  Despeckling of medical ultrasound images , 2006, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[17]  Gary F. Margrave,et al.  Application of median filtering in Kirchhoff migration of noisy data , 1999 .

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

[19]  Torbjørn Eltoft,et al.  Homomorphic wavelet-based statistical despeckling of SAR images , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Justin Domke,et al.  Gabor Filter Visualization , 2005 .

[21]  Savita Gupta,et al.  Image Denoising Using Wavelet Thresholding , 2002, ICVGIP.

[22]  N. Hamdy,et al.  Iris identification based on log Gabor filtering , 2003, 2003 46th Midwest Symposium on Circuits and Systems.