Performance Comparison of Various Filters on Despeckling of Medical Ultrasound Imaging
AUTHORS
Ankur Bhardwaj,KIET Group of Institutions, Ghaziabad, India
Ayush Goyal,Amity School of Engineering & Technology, Noida, India
AnandPrakash Shukla,
ABSTRACT
Ultrasound Imaging plays vital role in diagnoses a disease. US image suffers from speckle noise. Despeckling is an important task for accurate diagnosis. In this paper experiment has been performed to measure the effectiveness of various filters available for despeckling. Results are compared qualitatively and quantitatively the Peak Signal to Noise Ratio and SSIM parameters are used to quantify the results. On the basis of these parameters the performance of various filters are shown.
KEYWORDS
PSNR, SSIM, Speckle Noise, Median Filter
REFERENCES
[1] R.F. Wagner, S.W. Smith, J.M. Sandrik and H. Lopez, “Statistics of speckle in ultrasound B-Scans,”IEEE Transactions on Sonics Ultrasonic’s, Vol. 30, pp. 156-163, May, (1983).
[2] T. Loupas, W.N. McDicken and P.L. Allan, "An adaptive weighted median filter for speckle suppression in medical ultrasonic images", Circuits and Systems, IEEE Transactions on, Vol. 36, No.1, pp. 129-135, (1989).
[3] Y. Chen, R. Yin, P. Flynn and S. Broschat, “Aggressive region growing for speckle reduction inultrasound images”, Pattern Recognition Letters, Vol. 24, No. 4, pp. 677-691, (2003).
[4] J.I. Koo and S.B. Park, "Speckle reduction with edge preservation in medical ultrasonic images using a homogeneous region growing mean filter (HRGMF)", Ultrasonic imaging, Vol. 13, No. 3, pp.211-237, (1991).
[5] F. Jin, "Adaptive Wiener filtering of noisy images and image sequences", Image Processing, ICIP 2003, Proceedings 2003 International Conference on, IEEE, Vol. 3, (2003).
[6] V. Shrimali, "Medical feature based evaluation of structuring elements for morphological enhancement of ultrasonic images", Journal of medical engineering & technology, Vol. 33, No. 2, pp.158-169, (2009).
[7] S. Gupta, R.C. Chauhan and S.C. Sexana. "Wavelet-based statistical approach for speckle reduction in medical ultrasound images", Medical and Biological Engineering and computing, Vol. 42, No. 2, pp. 189-192, (2004).
[8] S. Sudha, G.R. Suresh and R. Sukanesh, "Speckle noise reduction in ultrasound images by wavelet thresholding based on weighted variance", International journal of computer theory and engineering, Vol. 1, No. 1, pp. 7, (2009).
[9] A. Achim, A. Bezerianos and P. Tsakalides, "Novel Bayesian multi scale method for speckle removal in medical ultrasound images", Medical Imaging, IEEE Transactions on, Vol. 20, No. 8, pp.772-783, (2001).
[10] Abd-Elmoniem, Z. Khaled, A.B.M. Youssef and Y.M. Kadah, "Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion", Biomedical Engineering, IEEE Transactions on, Vol. 49, No. 9, pp. 997-1014, (2002).
[11] Y. Yu and S.T. Acton, "Speckle reducing anisotropic diffusion", Image Processing, IEEE Transactions on, Vol. 11, No. 11, pp. 1260-1270, (2002).
[12] Y. Yu, J.A. Molloy and S.T. Acton, "Three-dimensional speckle reducing anisotropic diffusion", Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on, IEEE, Vol. 2, (2003).
[13] Krissian, Karl, "Oriented speckle reducing anisotropic diffusion." Image Processing, IEEE Transactions on, Vol. 16, No. 5, pp. 1412-1424, (2007).
[14] Denis, Loi'c, "SAR image regularization with fast approximate discrete minimization", ImageProcessing, IEEE Transactions on, Vol. 18, No. 7, pp. 1588-1600, (2009).
[15] [15] Sarti, Alessandro, "Maximum likelihood segmentation of ultrasound images with Rayleigh distribution", Ultrasonic’s, Ferroelectrics, and Frequency Control, IEEE Transactions on, Vol. 52, No.6, pp. 947-960, (2005).
[16] A. Gregorio, "A suitable threshold for speckle reduction in ultrasound images", Instrumentation and Measurement, IEEE Transactions on, Vol. 62, No. 8, pp. 2270-2279, (2013).
[17] Y. Guo, Y. Wang and T. Hou, "Speckle filtering of ultrasonic images using a modified non local-based algorithm", Biomedical Signal Processing and Control, Vol. 6, No. 2, pp. 129-138, (2011).
[18] B. Deka and P.K. Bora, "Removal of correlated speckle noise using sparse and over complete representations", Biomedical Signal Processing and Control, Vol. 8, No. 6, pp. 520-533, (2013).
[19] W. Guodong, "Ultrasound image denoising using backward diffusion and frame let regularization", Biomedical Signal Processing and Control, Vol. 13, pp. 212-217, (2014).