RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients

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 (fieldprogrammable 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.INDEX TERMS Medical image processing, discrete wavelet transform, scaled filter coefficients, residue number system, high-performance computing, hardware accelerator, field-programmable gate arrays.

Medical Image Segmentation Algorithm Based on Markup Improvement Combining the Watershed Algorithm and the Level Set Algorithm

“…Despite the better results, problems such as illumination and incorrect edge positioning still exist and cannot be avoided. In reference [8] , h-minima technology was integrated with fuzzy distance transformation for watershed segmentation to create markers, but certain pseudo-minima values remained in this technique. In literature [9] , a morphological multiscale gradient approach was proposed, which handled step edges and fuzzy edges well, but did not completely remove false edges.…”

Modern Trends in Improving the Technical Characteristics of Devices and Systems for Digital Image Processing

“…The cases of 8-, 12-, and 16-bit images are considered. In [37], a scaling parameter for filter coefficients is introduced and a scheme for digital wavelet filtering of images with reduced computational complexity is proposed.…”

“…The authors of [69] proposed a computational approach based on RNS with reduced hardware costs by increasing the energy consumption. Work [37] presents RNS-based method for wavelet processing of 3D medical images. RNS increased the computation speed by increasing the hardware costs.…”