Work place: Department of Education, Bangabandhu Sheikh Mujibur Rahman Digital University, Bangladesh
E-mail: farhana@edu.bdu.ac.bd
Website:
Research Interests: Computational Learning Theory, Image Compression, Image Manipulation, Image Processing, Data Structures and Algorithms
Biography
Farhana Islam received her B.Sc and M.Sc degrees in Information Technology from Jahangirnagar University, Bangladesh in 2015 and 2016, respectively. Currently, she is working as a lecturer in the Department of Education at Bangabandhu Sheikh Mujibur Rahman Digital University, Bangladesh (BDU). Prior to joining BDU, she worked as an Assistant Lecturer at Gono Bishwabidyalay. Her research interests include IoT, machine learning, big data, image processing, and wireless communication.
By Md. Habibur Rahman Md. Selim Hossain Farhana Islam
DOI: https://doi.org/10.5815/ijigsp.2023.03.03, Pub. Date: 8 Jun. 2023
Ultrasound is mostly used for diagnosis to deal with the specific abnormality in human body. To observe the internal organs including liver, kidneys, pancreas, thyroid gland, ovaries etc. ultrasound can be used. In diagnostic applications, 2 to 18 MHz frequencies are used. The sound wave explorations occurred through soft tissue and fluids. It bounces back as echoes from denser surfaces and creates an image. While producing ultrasound images from echo signal speckle noise is induced in a multiplicative way. Thus, speckle becomes the key challenge for ultrasound imaging. Several speckle reducing linear, non-linear and anisotropic diffusion-based methods are implemented to preserve the sharp edges of ultrasound images. Those methods contain lake of smoothing and edge preservation. However, this research proposed a combined method of adaptive filter (wiener) and anisotropic diffusion (modified Perona Malik) for speckle reduction of 2D ultrasound images by retain the important anatomical features. A comparison of all the existing methods studied based on the simulated experiment. To test the methods liver, kidney, heart and pancreas noise free images are used. Then, speckle noise is manually added with distinguished variance in between 0.02 and 0.20. Quality metrics are used to test the performance and show the improvements of the proposed method. About 71.79% structure similarity (SSIM), 66.72% root mean square error (RMSE), 56.93% signal to noise ratio (SNR), and 62.30% computational time are improved on average compared with the other methods.
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