Ultrasound Compressive Sensing Using BCS-FOCUSS

Yousufi, Musyyab; Amir, Muhammad; Javed, Umer; Tayyib, Muhammad; Abdullah, Suheel

doi:10.1109/compcomm.2018.8780883

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…To avoid the blocking artifacts upon reconstruction, Guided Frequency Filter (GFF) is proposed. In our previous work we have proposed to remove these artifacts using median filter [24]. The reconstruction performance is improved using GFF filter [23].…”

Section: Block Compressed Sensingmentioning

confidence: 99%

Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

Awan

Amir

Maqsood

et al. 2021

ITC

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Fetal ECG extraction from abdominal ECG is critical task for telemonitoring of fetus which require lot of understanding to the subject. Conventional source separation methods are not efficient enough to separate FECG from huge multichannel ECG. Thus use of compression technique is needed to compress and reconstruct ECG signal without any significant losses in quality of signal. Compressed sensing shows promising results for such tasks. However, current compressed sensing theory is not so far that successful due to the non-sparsity and strong noise contamination present in ECG signal. The proposed work explores the concept of block compressed sensing to reconstruct non-sparse FECG signal using GFOCUSS algorithm. The main objective of this paper is not only to successfully reconstruct the ECG signal but to efficiently separate FECG from abdominal ECG. The proposed algorithm is explained in very extensive manner for all experiments. The key feature of proposed method is, that it doesn’t affect the interdependence relation between multichannel ECG. The useof walsh sensing matrix made it possible to achieve high compression ratio. Experimental results shows that even at very high compression ratio, successful FECG reconstruction from raw ECG is possible. These results are validated using PSNR, SINR, and MSE. This shows the framework, compared to other algorithms such as current blocking CS algorithms, rackness CS algorithm and wavelet algorithms, can greatly reduce code execution time during data compression stage and achieve better reconstruction in terms of MSE, PSNR and SINR.

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Section: Block Compressed Sensingmentioning

confidence: 99%

Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

Awan

Amir

Maqsood

et al. 2021

ITC

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“…The signal x can be specified as x = ∑ i =1 N s i Ψ i , where s i symbolizes the coefficients of N × 1 vector s and Ψ i symbolizes the columns of matrix Ψ N × N . s i are the coefficient sequences of x and N are the total coefficients of x [16]. In common, Ψ is named transform domain, sparsifying dictionary, or sparse domain [17, 18].…”

Section: Compressive Sensingmentioning

confidence: 99%

Application of Compressive Sensing to Ultrasound Images: A Review

Yousufi

Amir

Javed

et al. 2019

BioMed Research International

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Compressive sensing (CS) offers compression of data below the Nyquist rate, making it an attractive solution in the field of medical imaging, and has been extensively used for ultrasound (US) compression and sparse recovery. In practice, CS offers a reduction in data sensing, transmission, and storage. Compressive sensing relies on the sparsity of data; i.e., data should be sparse in original or in some transformed domain. A look at the literature reveals that rich variety of algorithms have been suggested to recover data using compressive sensing from far fewer samples accurately, but with tradeoffs for efficiency. This paper reviews a number of significant CS algorithms used to recover US images from the undersampled data along with the discussion of CS in 3D US images. In this paper, sparse recovery algorithms applied to US are classified in five groups. Algorithms in each group are discussed and summarized based on their unique technique, compression ratio, sparsifying transform, 3D ultrasound, and deep learning. Research gaps and future directions are also discussed in the conclusion of this paper. This study is aimed to be beneficial for young researchers intending to work in the area of CS and its applications, specifically to US.

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Sparse Approximation Techniques for Efficient Medical Image Representations

Datta

Roy

2021

Technical Advancements of Machine Learning in Healthcare

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Ultrasound Compressive Sensing Using BCS-FOCUSS

Cited by 5 publications

References 20 publications

Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

Application of Compressive Sensing to Ultrasound Images: A Review

Sparse Approximation Techniques for Efficient Medical Image Representations

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