Abstract
The dominant degrading factor of quality in ultrasound images is mainly due to the occurrence of speckle noise that in turn leads to false ameliorative decisions, restricts auto diagnosis and telemedicine practices. In medical image analysis speckle reduction is contemplated to be the pre-processing task that sustains decisive information and exclude speckle noise. Meta-heuristics optimization algorithm were used now a days for speckle reduction problems. Our contribution in this paper analyses the use of optimization technique in determining the best noise removing filter coefficients that removes the speckle content contributively. The proposed method comprises the use of Finite Impulse Response filter receiving the filter coefficients from Tree Seed optimization algorithm. Evaluation of noise removal with standard metrics such as Peak Signal to Noise Ratio, Correlation coefficient and Structural Similarity Index shows that the proposed method gives optimal speckle reduction score when compared with conventional filters and its superiority in despeckling medical ultrasound images. Assessment of the proposed methodology with advanced evaluation metrics ensures the ability of it in terms of preserving edges and textural features.
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References
Rosa, R., Monteiro, F.C.: Performance analysis of speckle ultrasound image filtering. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 1163, 1–9 (2014)
Luizou, C.P., Pattichis, C.S., Christodouluo, C.I., Istepanian, R.S.H., Pantziaris, M., Nicolaides, A.: Comparative evaluation of despecle filtering in ultrasound imaging of the carotid artery. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 52, 1653–1669 (2005)
Andria, G., Attivissimo, F., Cavone, G., Giaquinto, N., Lanzolla, A.M.L.: Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images. Meas. J. Int. Meas. Confed. 45, 1792–1800 (2012)
Chandra, A., Chattopadhyay, S.: A new strategy of image denoising using multiplier-less FIR filter designed with the aid of differential evolution algorithm. Multimed. Tools Appl. 75, 1079–1098 (2016)
Maity, A., Pattanaik, A., Sagnika, S., Pani, S.: A comparative study on approaches to speckle noise reduction in images. In: Proceedings of 1st International Conference Computer Communications and Networks CINE, pp. 148–15 (2015)
Gupta, D., Anand, R.S., Tyagi, B.: Ripplet domain non-linear filtering for speckle reduction in ultrasound medical images. Biomed. Signal Process. Control 10, 79–91 (2014)
Reddy, K.S., Sahoo, S.K.: An approach for FIR filter coefficient optimization using differential evolution algorithm. AEU Int. J. Electron. Commun. 69, 101–108 (2015)
Mohammadi, A., Zahiri, S. H.: Analysis of Swarm Intelligence and Evolutionary Computation Techniques in IIR Digital Filters Design. In: 2016 1st Conference Swarm Intelligent Evolution Computer. pp. 64–69 (2016)
Roomi, S.M.M., Rajee, R.B.J.: Speckle noise removal in ultrasound images using Particle Swarm Optimization technique. In: 2011 International Conference Recent Trends Information Technology, pp. 926–931 (2011)
Kiran, M.S.: TSA: Tree-seed algorithm for continuous optimization. Expert Syst. Appl. 42, 6686–6698 (2015)
Muneeswaran, V., Pallikonda Rajasekaran, M.: Performance Evaluation of Radial Basis Function Networks Based on Tree Seed Algorithm. In: International Conference Circuit, Power Computer Technology [ICCPCT] (2016)
Zosso, D., Bustin, A.: A primal-dual projected gradient algorithm for efficient Beltrami regularization. UCLA CAM Report, pp. 14–52 (2014)
De Paiva, J.L., Toledo, C.F.M., Pedrini, H.: An approach based on hybrid genetic algorithm applied to image denoising problem. Appl. Soft Comput. (2015)
Latifoglu, F.: A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: an ultrasound image application. Comput. Methods Programs Biomed. 111, 561–569 (2013)
Haddad, R.A., Akansu, A.N.: Class of fast Gaussian binomial filters for speech and image processing. IEEE Trans. Signal Process. 39, 717–721 (1991)
Mahalanabis, A.K.: Introduction to random signal analysis and kalman filtering. Robert G. Brown. Automatica. 22(3), 387–388 (1986)
Sen Lee, J.: Speckle analysis and smoothing of synthetic aperture radar images. Comput. Graph. Image Process. 17(1), 24–32 (1981)
Hua, J., Kuang, W., Gao, Z., Meng, L., Xu, Z.: Image denoising using 2-D FIR filters designed with DEPSO. Multimed. Tools Appl. 69(1), 157–169 (2014)
Acknowledgements
The authors thank Dr. P. VIJAY BABU, M.B.B.S., D.M.R.D., Consultant Radiologist, VIJAY SCANS-Rajapalayam, Tamilnadu, for supporting the research by providing Ultrasound images and necessary patient information.
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Muneeswaran, V., Pallikonda Rajasekaran, M. (2018). Beltrami-Regularized Denoising Filter Based on Tree Seed Optimization Algorithm: An Ultrasound Image Application. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1. ICTIS 2017. Smart Innovation, Systems and Technologies, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-319-63673-3_54
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DOI: https://doi.org/10.1007/978-3-319-63673-3_54
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