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Reconstruction of a Multiscale Filter for Edge Preserving Speckle Suppression of Ultrasound Images
Mehedi Hasan Talukder, Md. Masudur Rahman, Shisir Mia, Mohammad Motiur Rahman
Pages - 11 - 27     |    Revised - 30-06-2022     |    Published - 01-08-2023
Volume - 17   Issue - 2    |    Publication Date - August 2023  Table of Contents
Linear Filter, Non-linear Filter, Speckle Noise, Gabor Filter, Medical Images.
Speckle noise tends to reduce the diagnostic value of ultrasound imaging modalities by degrading image quality. Edge-preserving noise-suppression can play an important role for accurate diagnosis.Therefore edge-preserving speckle suppression is the ultimate demand for accurate diagnosisby healthcare industries. In this study, a new hybrid filtering technique, namely, multiscale filter is proposed and analyzed to suppress the speckle noise in ultrasound images by preserving the image edges. Linear filtering speeds are high, but cannot preserve the edges of images efficiently, and this is a major limitation. Conversely, nonlinear filtering can handle edges more effectively; a Gabor filter preserves edges well but fails at suppressing noise. The method proposed here combines the concept of three linear and nonlinear filters with a Gabor filter to counter the limitations. In particular, when it is filtered, a 3×3 image kernel is divided into three segments and three linear and non-linear techniques are applied to each segment. Finally, the results of each section are integrated and processing is performed with a Gabor filter to obtain the results. The performance of the multiscale filter is analyzed for various ultrasound images of kidney, breast, abdomen, prostrate, orthopedic, and liver. The proposed multiscale filter provides superior results than other widely used de-speckling filters.
Abd El-Gwad, G. N. H., & Omar, Y. M. (2017). Selection of the best despeckle filter of ultrasound images. In 2017 2nd International Conference on Multimedia and Image Processing (ICMIP) (pp. 245-249). IEEE.https://doi.org/10.1109/ICMIP.2017.46.
Benkrid, K., Crookes, D., & Benkrid, A. (2002). Design and implementation of a novel algorithm for general purpose median filtering on FPGAs. In 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No. 02CH37353) (Vol. 4, pp. IV-IV). IEEE. https://doi.org/10.1109/ISCAS.2002.1010482.
Bhattacharya, D., Devi, J., & Bhattacherjee, P. (2013). Brain image segmentation technique using Gabor filter parameter. Am. J. Eng. Res, 2(9), 127-132.
Buades, A., Coll, B., & Morel, J. M. (2005). A review of image denoising algorithms, with a new one. Multiscale modeling & simulation, 4(2), 490-530.
Church, J. C., Chen, Y., & Rice, S. V. (2008). A spatial median filter for noise removal in digital images. In IEEE SoutheastCon 2008 (pp. 618-623). IEEE. https://doi.org/10.1109/SECON.2008.4494367.
Czerwinski, R. N., Jones, D. L., & O'Brien, W. D. (1995). Ultrasound speckle reduction by directional median filtering. In Proceedings., International Conference on Image Processing (Vol. 1, pp. 358-361). IEEE.https://doi.org/10.1109/ICIP.1995.529720.
Dass, R. (2018). Speckle noise reduction of ultrasound images using BFO cascaded with wiener filter and discrete wavelet transform in homomorphic region. Procedia computer science, 132, 1543-1551. https://doi.org/10.1016/j.procs.2018.05.118.
Duarte-Salazar, C. A., Castro-Ospina, A. E., Becerra, M. A., & Delgado-Trejos, E. (2020). Speckle noise reduction in ultrasound images for improving the metrological evaluation of biomedical applications: an overview. IEEE Access, 8, 15983-15999. doi: 10.1109/ACCESS.2020.2967178.
Dutt, V., & Greenleaf, J. F. (1996). Adaptive speckle reduction filter for log-compressed B-scan images. IEEE Transactions on Medical Imaging, 15(6), 802-813. https://doi.org/10.1109/42.544498.
Fernández-Caballero, A., & Mateo, J. L. (2008). Methodological approach to reducing speckle noise in ultrasound images. In 2008 International Conference on BioMedical Engineering and Informatics (Vol. 2, pp. 147-154). IEEE. https://doi.org/10.1109/BMEI.2008.328.
Garg, A., & Khandelwal, V. (2018). Combination of spatial domain filters for speckle noise reduction in ultrasound medical images. Advances in Electrical and Electronic Engineering, 15(5), 857-865.https://doi.org/10.15598/aeee.v15i5.2288.
Gonzalez, R. C. (2009). Digital image processing. Pearson education, Prentice-Hall.
Guo, Y., Cheng, H. D., Tian, J., & Zhang, Y. (2009). A novel approach to speckle reduction in ultrasound imaging. Ultrasound in medicine & biology, 35(4), 628-640. https://doi.org/10.1016/j.ultrasmedbio.2008.09.007.
Gupta, B., & Negi, S. S. (2013). Image denoising with linear and non-linear filters: A review. International Journal of Computer Science Issues (IJCSI), 10(6), 149.
Gupta, M., & Garg, A. (2017). An efficient technique for speckle noise reduction in ultrasound images. In 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 177-180). IEEE.https://doi.org/10.1109/SPIN.2017.8049939.
Iswardani, A., & Hidayat, W. (2018). "Mammographic Image Enhancement using Digital Image Processing Technique", International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 5.
Joseph, S., Balakrishnan, K., Nair, M. B., & Varghese, R. R. (2013). Ultrasound image despeckling using local binary pattern weighted linear filtering. International Journal of Information Technology and Computer Science, 5(6), 1-9.https://doi.org/10.5815/ijitcs.2013.06.01.
Karaman, M., Kutay, M. A., & Bozdagi, G. (1995). An adaptive speckle suppression filter for medical ultrasonic imaging. IEEE Transactions on medical imaging, 14(2), 283-292. https://doi.org/10.1109/42.387710.
Khan, M. A., Sharif, M., Akram, T., Raza, M., Saba, T., & Rehman, A. (2020). Hand-crafted and deep convolutional neural network features fusion and selection strategy: an application to intelligent human action recognition. Applied Soft Computing, 87, 105986.
KumarPatidar, P., Lalit, L., Singh, B., & Bagaria, G. (2014). Image filtering using linear and non linear filter for gaussian noise. International Journal of Computer Applications, 93(8), 29-34. https://doi.org/10.5120/16237-5760.
Mia, S., Talukder, M. H., & Rahman, M. M. (2023). RobustDespeckling: Robust speckle noise reduction method using multi-scale and kernel fisher discriminant analysis. Biomedical Engineering Advances, 5, 100085. https://doi.org/10.1016/j.bea.2023.100085.
Nadji, Y., Mbainaibeye, J., & Toussaint, G., (2023), “Contribution to the Pre-processing Method for Image Quality Improving: Application to Mammographic Images” International Journal of Image Processing, (pp. 1-10), 17(1).
Narayanan, S. K., & Wahidabanu, R. S. D. (2009). A view on despeckling in ultrasound imaging. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2(3), 85-98.
Negi, N., & Mathur, S. (2014). An improved method of edge detection based on Gabor wavelet transform. Recent Advances in Electrical Engineering And Electronic Devices; WSEAS: Geneva, Switzerland, 184-191.
Njeh, I., Sassi, O. B., Chtourou, K., & Mida, A. B. H. (2011). Speckle noise reduction in breast ultrasound images: SMU (SRAD median unsharp) approch. In Eighth International Multi-Conference on Systems, Signals & Devices (pp. 1-6). IEEE. https://doi.org/10.1109/SSD.2011.5981429
Park, H., Miyazaki, R., Nishimura, T., & Tamaki, Y. (2007). The speckle noise reduction and the boundary enhancement on medical ultrasound images using the cellular neural networks. IEEJ Transactions on Electronics, Information and Systems, 127(10), 1726-1731. https://doi.org/10.1541/ieejeiss.127.1726.
Patidar, P., Gupta, M., Srivastava, S., & Nagawat, A. K. (2010). Image de-noising by various filters for different noise. International journal of computer applications, 9(4), 45-50. http://dx.doi.org/10.5120/1370-1846.
Penney, G. P., Blackall, J. M., Hamady, M. S., Sabharwal, T., Adam, A., & Hawkes, D. J. (2004). Registration of freehand 3D ultrasound and magnetic resonance liver images. Medical image analysis, 8(1), 81-91.https://doi.org/10.1016/j.media.2003.07.003.
Pitas, I., & Venetsanopoulos, A. N. (1992). Order statistics in digital image processing. Proceedings of the IEEE, 80(12), 1893-1921. https://doi.org/10.1109/5.192071.
PK, M. K., Arefin, M. G., Rahman, M. M., & Hossain, A. D. (2014). Automatically gradient threshold estimation of anisotropic diffusion for meyer's watershed algorithm based optimal segmentation. International Journal of Image, Graphics and Signal Processing, 6(12), 26.
Pradeep, S., & Nirmaladevi, P. (2021). A review on speckle noise reduction techniques in ultrasound medical images based on spatial domain, transform domain and CNN methods. In IOP Conference Series: Materials Science and Engineering (Vol. 1055, No. 1, p. 012116). IOP Publishing. doi: 10.1088/1757-899X/1055/1/012116.
Pregitha, R. E., Jegathesan, V., & Selvakumar, C. E. (2012). Speckle noise reduction in ultrasound fetal images using edge preserving adaptive shock filters. International Journal of Scientific and Research Publications, 2(3), 1-3.
Rahman, M. M., Abdul Aziz, M. K. P., Rajiv, M. A. N. U., & Uddin, M. S. (2012). An optimized speckle noise reduction filter for ultrasound images using anisotropic diffusion technique. International journal of Imaging, 8(2).
Rahman, M. M., PK, M. K., & Uddin, M. S. (2014). Optimum Threshold Parameter Estimation of Wavelet Coefficients Using Fisher Discriminant Analysis for Speckle Noise Reduction. International Arab Journal of Information Technology (IAJIT), 11(6).
Rahman, M. M., PK, M. K., Aziz, A., Arefin, M. G., & Uddin, M. S. (2013). Adaptive anisotropic diffusion filter for speckle noise reduction for ultrasound images. International Journal of Convergence Computing, 1(1), 50-59. https://dx.doi.org/10.1504/IJCONVC.2013.054657.
Randhawa, S. K., & Sunkaria, R. K. (2017). A comparative analysis of various speckle reduction techniques for ultrasound images. In 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC) (pp. 106-110). IEEE. https://doi.org/10.1109/ISPCC.2017.8269659.
Roomi, S. M. M., & Rajee, R. J. (2011). Speckle noise removal in ultrasound images using particle swarm optimization technique. In 2011 International Conference on Recent Trends in Information Technology (ICRTIT) (pp. 926-931). IEEE. https://doi.org/10.1109/ICRTIT.2011.5972404.
Ruhaiyem,, N. I. R., & Hammade, N. A. (2021). Cerebrovascular Segmentation Based on Edge Preserving Filters Technique in Magnetic Resonance Angiography Images: A Systematic Review. International Journal of Image Processing, (pp. 48-67), 15(4).
Sivakumar, R., Gayathri, M. K., & Nedumaran, D. (2010). Speckle filtering of ultrasound B-Scan Images-a comparative study between spatial and diffusion filters. In 2010 IEEE Conference on Open Systems (ICOS 2010) (pp. 80-85). IEEE. https://doi.org/10.1109/ICOS.2010.5720068.
Sudha, S., Suresh, G. R., & Sukanesh, R. (2009). Speckle noise reduction in ultrasound images by wavelet thresholding based on weighted variance. International journal of computer theory and engineering, 1(1), 7.
Talukder, M. H., & Rahman, M. M. (2015). Despeckling 3D Ultrasound Images using Linear Regression. In International Conference on Materials, Electronics & Information Engineering, ICMEIE-2015.
Talukder, M. H., Islam, M. A., Ghosh, T. K., & Rahman, M. M. (2013). A new filtering technique for reducing speckle noise from ultrasound images. International Journal of Research in Computer and Communication Technology (IJRCCT), 2(9), 685-688.
Talukder, M. H., Ogiya, M., & Takanokura, M. (2018). Hybrid technique for despeckling medical ultrasound images. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 1, pp. 358-363).
Talukder, M. H., Ogiya, M., & Takanokura, M. (2018). New Noise Reduction Technique for Medical Ultrasound Imaging using Gabor Filtering. International Journal of Image Processing (IJIP), 12(1), 28.
Xu, Y., Weaver, J. B., Healy, D. M., & Lu, J. (1994). Wavelet transform domain filters: a spatially selective noise filtration technique. IEEE transactions on image processing, 3(6), 747-758. https://doi.org/10.1109/83.336245.
Yang, J., Li, J., & Wang, H. (2019). A Seismic Signal Denoising Method Based on Wavelet Comprehensive Threshold. In ITM Web of Conferences (Vol. 25, p. 01014). EDP Sciences.
Yu, Y., & Acton, S. T. (2002). Speckle reducing anisotropic diffusion. IEEE Transactions on image processing, 11(11), 1260-1270.https://doi.org/10.1109/TIP.2002.804276.
Yue, Y., & Clark Jr, J. W. (2007). Speckle suppression for 3-D ultrasound images using nonlinear multiscale wavelet diffusion. In Medical Imaging 2007: Ultrasonic Imaging and Signal Processing (Vol. 6513, pp. 290-298). SPIE. https://doi.org/10.1117/12.710055.
Zong, X., Laine, A. F., & Geiser, E. A. (1998). Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing. IEEE transactions on medical imaging, 17(4), 532-540.https://doi.org/10.1109/42.730398.
Dr. Mehedi Hasan Talukder
Department of Computer Science and Engineering, MawlanaBhashani Science and Technology University, Tangail, 1902 - Bangladesh
Dr. Md. Masudur Rahman
Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3814 - Bangladesh
Dr. Shisir Mia
Department of Computer Science and Engineering, MawlanaBhashani Science and Technology University, Tangail, 1902 - Bangladesh
Dr. Mohammad Motiur Rahman
Department of Computer Science and Engineering, MawlanaBhashani Science and Technology University, Tangail, 1902 - Bangladesh

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