Application of Compressive Sensing to Ultrasound Images: A Review.
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 stora...
| Publicado en: | BioMed Research International pp. 1 - 15 |
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| Autores principales: | , , , , , , , , , |
| Formato: | equations & formulas pictorial review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
11/15/2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141408341&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141408341 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 11/15/2019 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 141408341 141408341 141408341 10.1155/2019/7861651 141408341 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Application of Compressive Sensing to Ultrasound Images: A Review. aug: au: Yousufi, Musyyab Amir, Muhammad Javed, Umer Tayyib, Muhammad Abdullah, Suheel Ullah, Hayat Qureshi, Ijaz Mansoor Alimgeer, Khurram Saleem Akram, Muhammad Waseem Khan, Khan Bahadar affil: Faculty of Engineering and Technology, International Islamic University Islamabad, Islamabad 44000, Pakistan sug: subj: Ultrasonography Methods Imaging, Three-Dimensional Methods Signal Processing, Computer Assisted Algorithms Image Enhancement Image Processing, Computer Assisted Deep Learning ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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