Open Access
IEEE Access, volume 10, pages 19215-19231
RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients
Publication type: Journal Article
Publication date: 2022-02-15
General Materials Science
General Engineering
General Computer Science
Abstract
Medical imaging using different modalities has many problems. The main ones are low informativeness, various distortion noises, and a large amount of information. Fusion, denoising, and visual data compression are used to solve them in practice. Discrete wavelet transform is one way to implement various fusion, denoising, and compression methods for 2D and 3D medical image processing. Medical imaging systems produce increasingly accurate images with scanning technology and digital devices development. These images have improved quality using both higher spatial resolutions and color bit-depth. Processing a large volume of medical imaging data requires considerable resources and processing time. Modern wavelet-based devices for medical image processing do not meet the current performance demand. Hardware accelerators are being designed to solve this problem. This paper proposes new (field-programmable gate array) FPGA accelerators using wavelet processing (WP) with scaled filter coefficients (SFC) and parallel computing in residue number system (RNS) to improve the performance of high-quality 3D medical image WP systems. The computational complexity is reduced using the developed WP method with SFC and the proposed wavelet filter coefficients scaling algorithm. Parallel computing is organized in RNS using moduli sets of a particular type. Hardware implementation of 3D medical image WP using the proposed FPGA accelerators increases device performance by 2.89-3.59 times, increasing the hardware resources by 1.18-3.29 times compared to state-of-the-art solutions. The device performance improvement is achieved while maintaining high-quality 3D medical image processing in peak signal-to-noise ratio terms.
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Nagornov N. et al. RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients // IEEE Access. 2022. Vol. 10. pp. 19215-19231.
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Nagornov N., Lyakhov P., Valueva M., Bergerman M. V. RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients // IEEE Access. 2022. Vol. 10. pp. 19215-19231.
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TY - JOUR
DO - 10.1109/access.2022.3151361
UR - https://doi.org/10.1109%2Faccess.2022.3151361
TI - RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients
T2 - IEEE Access
AU - Nagornov, Nikolai
AU - Lyakhov, Pavel
AU - Valueva, Maria
AU - Bergerman, Maxim V
PY - 2022
DA - 2022/02/15 00:00:00
PB - IEEE
SP - 19215-19231
VL - 10
SN - 2169-3536
ER -
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@article{2022_Nagornov,
author = {Nikolai Nagornov and Pavel Lyakhov and Maria Valueva and Maxim V Bergerman},
title = {RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients},
journal = {IEEE Access},
year = {2022},
volume = {10},
publisher = {IEEE},
month = {feb},
url = {https://doi.org/10.1109%2Faccess.2022.3151361},
pages = {19215--19231},
doi = {10.1109/access.2022.3151361}
}