Research Article |
Innovative Noise Reduction Strategies in Ultrasound Images Using Shearlet Transform and Bayesian Thresholding
Author(s): Meena L C* and Joe Prathap P M
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 12, Issue 2
Publisher : FOREX Publication
Published : 25 June 2024
e-ISSN : 2347-470X
Page(s) : 605-610
Abstract
Uterine fibroids are prevalent benign tumors affecting women, often diagnosed through imaging modalities such as ultrasound. Ultrasound imaging is a widely used diagnostic modality for uterine fibroid due to its non-invasive nature. However, the images obtained often suffer from speckle noise, which can obscure fine details and complicate accurate diagnosis. Existing methods for removing speckle noise have limitations, including losing texture and edge information and not being able to handle low frequency noises. This paper presents a novel approach for speckle noise reduction by combining Shearlet Transform with Bayesian thresholding. The proposed method aims to achieve superior noise reduction while retaining important image features crucial for accurate diagnosis. Experimental results demonstrate the efficacy of the Shearlet Transform and Bayesian thresholding in significantly reducing speckle noise, enhancing image quality, and improving the interpretability of ultrasound images. Performance metrics like Mean Squared Error (MSE), Structural Similarity Index and Peak Signal to Noise Ratio (PSNR) helps to validate our proposed method. Reducing speckle noise in ultrasound images of uterine fibroids contributes to more accurate diagnosis and improves surgical treatment outcomes.
Keywords: Bayesian Thresholding
, Shearlet Transform
, Speckle Noise
, Ultrasound Imaging
, Uterine fibroids
.
Meena L C*, Research Scholar, Department of Computer Science and Engineering, R. M. D. Engineering College, Thiruvallur District, Affiliated by Anna University, Chennai, India; Email: lcmeena2008@gmail.com
Joe Prathap P M , IEEE Senior Member and Professor, Department of Computer Science and Engineering, R. M. D. Engineering College, Thiruvallur District, Affiliated by Anna University, Chennai, India; Email: drjoeprathap@rmd.ac.in
-
[1] Ragesh, N. K; Anil, A. R and Rajesh, R. Digital Image Denoising in Medical Ultrasound Images: A Survey. IGCST AIML – 11 Conference, Dubai, UAE, 2011, pp. 12-14.
-
[2] Loupas, T; Mcdiken, W. N and Allan, P. L. An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Trans. circuits and systems, 1989, Vol. 36, pp. 129-35.
-
[3] Michailovich, O. V and Allen Tannenbaum. Despeckling of medical ultrasound images. IEEE Trans. Ultrasonics. Ferroelectrics and frequency control, 2006, Vol. 53(1).
-
[4] Ahmed, L.J. Discrete Shearlet Transform Based Speckle Noise Removal in Ultrasound Images. Natl. Acad. Sci. Lett, 2018, Vol. 41, pp. 91–95. https://doi.org/10.1007/s40009-018-0620-7.
-
[5] Anchim, A; Tsakalides, P and Bezarianos, A. Novel bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans. on Medical Imaging, 2001, Vol. 20, pp. 772-783.
-
[6] Anju, T. S and Raj, N. R. N. Denoising of digital images using shearlet transform, IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 2016, pp. 893-896. doi: 10.1109/RTEICT.2016.7807957.
-
[7] Sidheswar, R; Prince P. M; Sunil K. S; Sampad, K. P; Palai G. A new image denoising framework using bilateral filtering based non-subsampled shearlet transform, Optik, 2020, Vol. 216, 164903.
-
[8] Febin, I. P and Jidesh P. Despeckling and enhancement of ultrasound images using non-local variational framework. Vis Comput. 2022, Vol. 38(4),1413-1426. doi: 10.1007/s00371-021-02076-8.
-
[9] Duarte-Salazar, C. A; Castro-Ospina, A. E; Becerra, M. A; Delgado-Trejos, E. Speckle noise reduction in ultrasound images for improving the metrological evaluation of biomedical applications: an overview. IEEE Access, Vol. 8, pp. 15983–15999. https://doi.org/10.1109/ACCESS.2020.2967178.
-
[10] Choi, H; Jeong, J. Speckle noise reduction for ultrasound images by using speckle reducing anisotropic diffusion and Bayes threshold. J Xray Sci Technol, 2019, Vol. 27(5), pp. 885-898. doi: 10.3233/XST-190515.
-
[11] Easley, G; Labate, D; Lim W-Q. Sparse directional image representations using the discrete shearlet transform. Appl Comput Harmon Anal, 2008, Vol. 25, pp. 25–46.
-
[12] Joe Prathap, P. M; Praveen K. V. et. al., Deep Learning Based Intelligent and Sustainable Smart Healthcare Application in Cloud-Centric IoT Computers. Materials & Continua, 2021, Vol. 66, No. 2, pp. 1987-2003.
-
[13] Joe Prathap, P. M.; Beevi, L. S.; Reddy, P. H. S. K. and Priya, G. B. S. Small Intestine Cancer Prediction using Deep Learning: A Comparative Study of CNNs and RNNs. 2024 Third International Conference on Distributed Computing and High-Performance Computing (DCHPC), Tehran, Iran, Islamic Republic of, pp. 1-5.
-
[14] Faouzi Benzarti; Hamid Amiri. Speckle Noise Reduction in Medical Ultrasound Images, 2012, Vol. 9(2), pp. 187-194.
-
[15] Hyunho Choi and Jechang Jeong. Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images. Symmetry, 2020, 12(6), pp. 938.
-
[16] Shisir Mia; Mehedi Hasan Talukder; and Mohammad Motiur Rahman. RobustDespeckling: Robust speckle noise reduction method using multi-scale and kernel fisher discriminant analysis. Biomedical Engineering Advances, 2023, Vol. 5, pp. 100085.