UltraCompression: Framework for High Density Compression of Ultrasound Volumes using Physics Modeling Deep Neural Networks

Ultrasound image compression by preserving speckle-based key information is a challenging task. In this paper, we introduce an ultrasound image compression framework with the ability to retain realism of speckle appearance despite achieving very high-density compression factors. The compressor emplo...

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Bibliographic Details
Published in:arXiv.org 2019-01
Main Authors: China, Debarghya, Francis, Tom, Nandamuri, Sumanth, Kar, Aupendu, Srinivasan, Mukundhan, Mitra, Pabitra, Sheet, Debdoot
Format: Article
Language:English
Subjects:
Artificial neural networks
Compression ratio
Density
Image compression
Image quality
Image segmentation
Image transmission
JPEG encoders-decoders
Statistical analysis
Statistical methods
Ultrasonic imaging
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Summary:Ultrasound image compression by preserving speckle-based key information is a challenging task. In this paper, we introduce an ultrasound image compression framework with the ability to retain realism of speckle appearance despite achieving very high-density compression factors. The compressor employs a tissue segmentation method, transmitting segments along with transducer frequency, number of samples and image size as essential information required for decompression. The decompressor is based on a convolutional network trained to generate patho-realistic ultrasound images which convey essential information pertinent to tissue pathology visible in the images. We demonstrate generalizability of the building blocks using two variants to build the compressor. We have evaluated the quality of decompressed images using distortion losses as well as perception loss and compared it with other off the shelf solutions. The proposed method achieves a compression ratio of \(725:1\) while preserving the statistical distribution of speckles. This enables image segmentation on decompressed images to achieve dice score of \(0.89 \pm 0.11\), which evidently is not so accurately achievable when images are compressed with current standards like JPEG, JPEG 2000, WebP and BPG. We envision this frame work to serve as a roadmap for speckle image compression standards.
ISSN:2331-8422