Massive Ultrasonic Data Compression Using Wavelet Packet Transformation Optimized by Convolutional Autoencoders

Ultrasonic signal acquisition platforms generate considerable amounts of data to be stored and processed, especially when multichannel scanning or beamforming is employed. Reducing the mass storage and allowing high-speed data transmissions necessitate the compression of ultrasonic data into a repre...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 34; no. 3; pp. 1395 - 1405
Main Authors: Wang, Boyang, Saniie, Jafar
Format: Journal Article
Language:English
Published: United States IEEE 01.03.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Acoustics
Algorithms
Beamforming
Biomedical imaging
Compression
Compression ratio
Convolution
Convolutional autoencoder (CAE)
Data compression
Data models
Data transmission
Decoding
Decomposition
Image coding
Image reconstruction
Kernels
Machine learning
Medical imaging
Nondestructive testing
Radio frequency
ultrasonic signals
Ultrasonic testing
unsupervised learning
wavelet packet transformation (WPT)
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Ultrasonic signal acquisition platforms generate considerable amounts of data to be stored and processed, especially when multichannel scanning or beamforming is employed. Reducing the mass storage and allowing high-speed data transmissions necessitate the compression of ultrasonic data into a representation with fewer bits. High compression accuracy is crucial in many applications, such as ultrasonic medical imaging and nondestructive testing (NDT). In this study, we present learning models for massive ultrasonic data compression on the order of megabytes. A common and highly efficient compression method for ultrasonic data is signal decomposition and subband elimination using wavelet packet transformation (WPT). We designed an algorithm for finding the wavelet kernel that provides maximum energy compaction and the optimal subband decomposition tree structure for a given ultrasonic signal. Furthermore, the WPT convolutional autoencoder (WPTCAE) compression algorithm is proposed based on the WPT compression tree structure and the use of machine learning for estimating the optimal kernel. To further improve the compression accuracy, an autoencoder (AE) is incorporated into the WPTCAE model to build a hybrid model. The performance of the WPTCAE compression model is examined and benchmarked against other compression algorithms using ultrasonic radio frequency (RF) datasets acquired in NDT and medical imaging applications. The experimental results clearly show that the WPTCAE compression model provides improved compression ratios while maintaining high signal fidelity. The proposed learning models can achieve a compression accuracy of 98% by using only 6% of the original data.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3105367