Sampling strategies for learning-based 3D medical image compression

Nagoor, Omniah H.; Whittle, Joss; Deng, Jingjing; Mora, Benjamin; Jones, Mark W.

doi:10.1016/j.mlwa.2022.100273

Cited by 2 publications

(1 citation statement)

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“…The major difficulty of IoT networks is that IoT may not have enough memory to manage all the transaction data in the IoT network. In [30], a proposed compression approach is presented to minimize the data traffic of the Internet of Things network. Therefore, it analyses various lossless compression algorithms, such as entropy-based or dictionary-based algorithms, and generic compression methods to identify which methodology or approach corresponds to IoT standards.…”

Section: Literature Reviewmentioning

confidence: 99%

Medical image compression and restoration using a hybrid neural network combining Huffman and meta-heuristic techniques

Khalifeh,

Taghizadeh,

Ghanbarian

et al. 2023

Preprint

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In order to compress medical pictures for long-term storage, two methods are used in this work. The first step is to use a neural network–based categorization system to simplify images using a hierarchical modeling technique. The Huffman cipher is then used to compress the reduced images. In the second method, a deep neural network is trained to make predictions. This method can potentially reduce the amount of data needed to describe a picture by using a trained neural network to make intelligent guesses about the location of individual pixels. Huffman compression is used to encrypt the remaining data. By using an improved spatial filtering method to the picture data, we can decode it and then use meta-heuristic algorithms like gray wolf optimization (GWO) and wild horse optimization (WHO) to rebuild the image. Without sacrificing data compression efficacy, this paves the way for a more practical implementation of the proposed techniques in cases when outcomes are uncertain. Images can be simplified using the suggested approaches, leading to faster decoding. Afterwards, performance metrics were taken and evaluated following predetermined daily procedures. The suggested approaches outperformed state-of-the-art deep learning-based systems in compressing medical images while maintaining an exceptionally high quality level.

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Section: Literature Reviewmentioning

confidence: 99%