The application of compressive sensing based on wavelet in the reconstruction of ultrasonic medical image

Li, Delai; Zhang, Qiong; Shen, Haihong; Shi, Jiabin; Shen, Minfen

doi:10.1109/mace.2011.5988201

Cited by 2 publications

(1 citation statement)

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“…Li et al [16] have investigated the usage of compressive sening on medical image reconstruction using measurement matrix for sparse reconstruction of an image. Panic et al [17] have presented a reconstruction algorithm using markov random approach in order to design a clustering mechanism over medical images.…”

Section: Introductionmentioning

confidence: 99%

OFCS: Optimized Framework of Compressive Sensing for Medical Images in Bottleneck Network Condition

Lakshminarayana¹,

Sarvagya²

2018

IJECE

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Compressive sensing is one of teh cost effective solution towards performing compression of heavier form of signals. We reviewed the existing research contribution towards compressive sensing to find that existing system doesnt offer any form of optimization for which reason the signal are superiorly compressed but at the cost of enough resources. Therefore, we introduce a framework that optimizes the performance of the compressive sensing by introducing 4 sequential algorithms for performing Random Sampling, Lossless Compression for region-of-interest, Compressive Sensing using transform-based scheme, and optimization. The contribution of proposed paper is a good balance between computational efficiency and quality of reconstructed medical image when transmitted over network with low channel capacity. The study outcome shows that proposed system offers maximum signal quality and lower algorithm processing time in contrast to existing compression techniuqes on medical images.

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Section: Introductionmentioning

confidence: 99%