Total views : 185
A Density Current Modeled Adaptive Weighted Average Despeckling Filter for Ultrasound Thyroid Images
Speckle noise reduction is an important preprocessing stage for ultrasound medical image processing. In this paper, an adaptive weighted average despeckling algorithm is proposed based on density currents equation. An analogy between the gravity current density model for sea breeze and speckle noise affected image is used and a denoising filter is designed. Suitable threshold values to distinguish between different regions in the image are selected for the denoising filter by considering the structural similarity and mean square error parameters of the particular image. Hence the proposed denoising filter adaptively changes the threshold values depending on nature of image and magnitude of noise in the image. For each pixel, weights have been changed adaptively by considering local neighbours in different directions. Experimental results of proposed method for natural images, Field II simulated images and real ultrasound images, show that the proposed method outperforms other existing methods in terms of signal to noise ratio, Structural similarity index and Ultrasound despeckling assessment index. It is also shown that proposed filter is able to improve the visual quality of Ultrasound images and better preserves image structural details compared to existing methods.
Density Current, Sea Breeze Equation, Speckle Noise, Ultrasound and Weighted Average.
- Dutt V, Greenleaf JF. Adaptive speckle reduction filter for log compressed B-scanimages. Transactions on Medical Imaging. 1996; 15(6):802–13.
- Abd-Elmoniem KZ, Youssef ABM, Kadah YM. Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion.IEEE Transactions on Biomedical Engineering. 2002; 49(9):997– 1014.
- Lee J. Digital image enhancement and noise filtering using local statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 1980; 2(2):165–8.
- Frost V, Stiles J, Shanmugan K,Holzman J. A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Transactions on Pattern Analysis and Machine. 1982; 4(2):157–66.
- Kuan DT, Sawchuk AA, Strand TC, Chavel P. Adaptive noise smoothing filter with signal-dependent noise. IEEE Transactions on Pattern Analysis and Machine Intelligence.1985; 7(2):165–77.
- Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence.1990; 12(7):629–39.
- Yu Y, Acton ST. Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing. 2002; 11(11):1260–70.
- Achim A, Bezerianos A, Tsakalides P. Novel bayesianmultiscale method for speckle removal in medical ultrasound images. IEEE Transactions on Medical Imaging. 2001; 20(8):772–83.
- Yue Y, Croitoru MM, Bidani A, Zwischenberger JB, Clark JW. Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images. IEEE Transactions on Medical Imaging. 2006; 25(3):297–311.
- Pizurica A, Philips W, Lemahieu I, Acheroy M. A versatile wavelet domain noisefiltration technique for medical imaging. IEEE Transactions on Medical Imaging. 2003; 22(3):323–31.
- Bhuiyan MIH, Ahmad MO, Swamy MNS. Spatially adaptive thresholding inwavelet domain for despeckling of ultrasound images. IET Image Processing. 2009; 3(3):147–62.
- Tomasi C, Manduchi R.Bilateral filtering for grey and colour images. IEEEInternational Conference on Computer Vision, Bombay; 1998. p.839–46.
- Farzana E, Tanzid M, Mohsin KM, Bhuiyan MIH, Hossain S. Adaptive bilateral filtering for despeckling of medical ultrasound images. Proceedings of IEEE- TENCON, Japan;2010. p. 1728–33.
- Donoho DL. Denoising by soft-thresholding. IEEE Transactions on Information Theory. 1995; 41(3):613–27.
- Benjamin TB. Gravity currents and related phenomena.Journal of FluidMechanics.1968; 31(2):209–48.
- Jensen JA. Field: A program for simulating ultrasound systems. Medical & Biological Engineering & Computing.1996; 4(1):351–3.
- Tay PC, Garson CD, Acton ST, Hossack JA. Ultrasound despeckling for contrastenhancement.IEEE Transactions on Image Processing. 2010; 19(7):1847–60.
- Wang Z, Bovika AC, Sheikh HR, Simoncelli EP. Image quality assessment: fromerror visibility to structural similarity.IEEE Transactions on Image Processing. 2004; 13(4):600– 12.
- Babu JJJ, Sudha GF. Non-subsampled contourlet transform based image Denoising in ultrasound thyroid images using adaptive binary morphological operations. IET Computer Vision. 2014; 8(6):718–28.
- Keerthivasan A, Babu JJJ, Sudha GF. Speckle noise reduction in ultrasound images using fuzzy logic based on histogram and directional differences. In IEEE International Conference on Communication and Signal Processing, India; 2013. p. 499–503.
- Babu JJJ, Sudha GF. Adaptive speckle reduction in ultrasound images using fuzzy logic on Coefficient of Variation.Biomedical Signal Processing and Control. 2016; 23:93– 103.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.