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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:BioMed research international Jg. 2019; H. 2019; S. 1 - 14
Hauptverfasser: Khan, Khan Bahadar, Akram, Muhammad Waseem, Qureshi, Ijaz Mansoor, Ullah, Hayat, Malik, Suheel Abdullah, Tayyib, Muhammad, Javed, Umer, Amir, Muhammad, Yousufi, Musyyab, Alimgeer, Khurram Saleem
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 2019
Hindawi
John Wiley & Sons, Inc
Schlagworte:
Algorithms
Compression
Compression ratio
Data Compression
Deep learning
Detection
Dictionaries
Fourier transforms
Humans
Image compression
Machine learning
Medical imaging
Medical imaging equipment
Neural networks
Review
Signal processing
Sparsity
Ultrasonic imaging
Ultrasonography
Ultrasound
Velocity
Pakistan
ISSN:2314-6133, 2314-6141, 2314-6141
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Review-3
content type line 23
Academic Editor: Elena Orlova
ISSN:2314-6133
2314-6141
2314-6141
DOI:10.1155/2019/7861651