sensors-logo

Journal Browser

Journal Browser

Special Issue "Compressed Sensing in Biomedical Signal and Image Analysis"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 March 2020.

Special Issue Editor

Prof. Dr. Sri Krishnan
E-Mail Website
Guest Editor
Department of Electrical, Computer & Biomedical Engineering, Ryerson University, Toronto, Canada
Interests: biomedical signal analysis, assistive technologies, machine learning, textile computing

Special Issue Information

Dear Colleagues,

Sampling of analog signals using the classical Shannon–Nyquist theorem has created and enhanced the digital world we all currently live in. Around 2004, researchers in the field of information theory published a series of seminal papers in which they demonstrated that provided that signals/images exhibit some sort of sparsity, they could be reconstructed using far fewer samples than the number typically needed with the classical sampling techniques. This led to the new paradigm of Compressive Sensing and a great deal of applications in all domains of the digital world. In this Special Issue, original papers are invited in the area of Compressive Sensing Applications to Biomedical Images and Signals. Biomedical instruments and systems could benefit tremendously from compressive sensing in many areas, such as efficient data acquisition, low-power sensing, solving inverse problems, sparse coding, machine learning, and distributed network sensing applications such as Internet of Things.

Prof. Dr. Sri Krishnan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sparsity
  • optimization
  • inverse problems
  • low-power devices
  • data acquisition
  • sampling
  • reconstruction
  • under-determined systems
  • medical imaging
  • healthcare IoT
  • physiological signals

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Improvement of Fast Model-Based Acceleration of Parameter Look-Locker T1 Mapping
Sensors 2019, 19(24), 5371; https://doi.org/10.3390/s19245371 - 05 Dec 2019
Abstract
Quantitative mapping is desirable in many scientific and clinical magneric resonance imaging (MRI) applications. Recent inverse recovery-look locker sequence enables single-shot T1 mapping with a time of a few seconds but the main computational load is directed into offline reconstruction, which can [...] Read more.
Quantitative mapping is desirable in many scientific and clinical magneric resonance imaging (MRI) applications. Recent inverse recovery-look locker sequence enables single-shot T1 mapping with a time of a few seconds but the main computational load is directed into offline reconstruction, which can take from several minutes up to few hours. In this study we proposed improvement of model-based approach for T1-mapping by introduction of two steps fitting procedure. We provided analysis of further reduction of k-space data, which lead us to decrease of computational time and perform simulation of multi-slice development. The region of interest (ROI) analysis of human brain measurements with two different initial models shows that the differences between mean values with respect to a reference approach are in white matter—0.3% and 1.1%, grey matter—0.4% and 1.78% and cerebrospinal fluid—2.8% and 11.1% respectively. With further improvements we were able to decrease the time of computational of single slice to 6.5 min and 23.5 min for different initial models, which has been already not achieved by any other algorithm. In result we obtained an accelerated novel method of model-based image reconstruction in which single iteration can be performed within few seconds on home computer. Full article
(This article belongs to the Special Issue Compressed Sensing in Biomedical Signal and Image Analysis)
Show Figures

Figure 1

Open AccessArticle
A Low-Complexity Compressed Sensing Reconstruction Method for Heart Signal Biometric Recognition
Sensors 2019, 19(23), 5330; https://doi.org/10.3390/s19235330 - 03 Dec 2019
Abstract
Biometric systems allow recognition and verification of an individual through his or her physiological or behavioral characteristics. It is a growing field of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method [...] Read more.
Biometric systems allow recognition and verification of an individual through his or her physiological or behavioral characteristics. It is a growing field of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method that has been proposed in recent years. The sampling and compression of data is completed synchronously, avoiding waste of resources and meeting the requirements of small size and limited power consumption of wearable portable devices. In this work, a compression reconstruction method based on compression sensing was studied using bioelectric signals, which aimed to increase the limited resources of portable remote bioelectric signal recognition equipment. Using electrocardiograms (ECGs) and photoplethysmograms (PPGs) of heart signals as research data, an improved segmented weak orthogonal matching pursuit (OMP) algorithm was developed to compress and reconstruct the signals. Finally, feature values were extracted from the reconstructed signals for identification and analysis. The accuracy of the proposed method and the practicability of compression sensing in cardiac signal identification were verified. Experiments showed that the reconstructed ECG and PPG signal recognition rates were 95.65% and 91.31%, respectively, and that the residual value was less than 0.05 mV, which indicates that the proposed method can be effectively used for two bioelectric signal compression reconstructions. Full article
(This article belongs to the Special Issue Compressed Sensing in Biomedical Signal and Image Analysis)
Show Figures

Figure 1

Open AccessArticle
3D Tensor Based Nonlocal Low Rank Approximation in Dynamic PET Reconstruction
Sensors 2019, 19(23), 5299; https://doi.org/10.3390/s19235299 - 01 Dec 2019
Abstract
Reconstructing images from multi-view projections is a crucial task both in the computer vision community and in the medical imaging community, and dynamic positron emission tomography (PET) is no exception. Unfortunately, image quality is inevitably degraded by the limitations of photon emissions and [...] Read more.
Reconstructing images from multi-view projections is a crucial task both in the computer vision community and in the medical imaging community, and dynamic positron emission tomography (PET) is no exception. Unfortunately, image quality is inevitably degraded by the limitations of photon emissions and the trade-off between temporal and spatial resolution. In this paper, we develop a novel tensor based nonlocal low-rank framework for dynamic PET reconstruction. Spatial structures are effectively enhanced not only by nonlocal and sparse features, but momentarily by tensor-formed low-rank approximations in the temporal realm. Moreover, the total variation is well regularized as a complementation for denoising. These regularizations are efficiently combined into a Poisson PET model and jointly solved by distributed optimization. The experiments demonstrated in this paper validate the excellent performance of the proposed method in dynamic PET. Full article
(This article belongs to the Special Issue Compressed Sensing in Biomedical Signal and Image Analysis)
Show Figures

Figure 1

Open AccessArticle
Single-Pixel Imaging with Origami Pattern Construction
Sensors 2019, 19(23), 5135; https://doi.org/10.3390/s19235135 - 23 Nov 2019
Abstract
Single-pixel compressive imaging can recover images from fewer measurements, offering many benefits especially for the imaging modalities where array detection is unavailable. However, the widely used random projections fail to explore internal relations between coding patterns and image reconstruction. Here, we propose a [...] Read more.
Single-pixel compressive imaging can recover images from fewer measurements, offering many benefits especially for the imaging modalities where array detection is unavailable. However, the widely used random projections fail to explore internal relations between coding patterns and image reconstruction. Here, we propose a single-pixel imaging method based on a deterministic origami pattern construction that can lead to a more accurate pattern ordering sequence and better imaging quality. It can decrease the sampling ratio, closer to the upper bounds. The experimental realization of this approach is a big step forward towards practical applications. Full article
(This article belongs to the Special Issue Compressed Sensing in Biomedical Signal and Image Analysis)
Show Figures

Figure 1

Back to TopTop