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Algorithms
Special Issues
Data Compression for the Life Sciences
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Data Compression for the Life Sciences

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (28 February 2014) | Viewed by 6268

Special Issue Editors

Prof. Dr. Khalid Sayood

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Guest Editor
Department of Electrical Engineering, University of Nebraska-Lincoln, 209N Scott Engineering Center, P.O. Box 880511, Lincoln, NE 68588-0511, USA
Interests: data compression; joint source-channel coding; bioinformatics; teaching and information
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Armando J. Pinho

E-Mail Website
Guest Editor
Department of Electronics Telecommunications and Informatics, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Interests: data compression; image compression; data models; Kolmogorov complexity and application of data models to computational biology

Special Issue Information

Dear Colleagues,

The life sciences have been generating an exponential volume of data, a trend that is certain to continue in the forthcoming years. Biomedical imaging, including digital radiology, magnetic resonance and computed tomography, currently contribute the most to this daily production of data. However, genomic data production is growing at a dramatic rate. Currently, while storage capacity is doubling every 18 months, the production of genomic data is doubling at twice that pace. It is the data deluge.

Although general-purpose compression tools are often used to reduce the volume of these data, the best results can only be achieved with specialized algorithms. Moreover, better compression algorithms not only give returns in terms of less storage and transmission time requirements, but potentially also in terms of a deeper understanding of the data source itself, because better compression algorithms imply better underlying data models. In this Special Issue of Algorithms, we seek original contributions to this exciting field, hoping that they will be a source of inspiration for current and forthcoming researchers.

Prof. Dr. Khalid Sayood
Dr. Armando J. Pinho
Guest Editors

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 submissions that pass pre-check are 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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • data compression
  • genomic data compression
  • biomedical image compression
  • biomedical signal compression
  • information theory

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Published Papers (1 paper)

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Article
Group Sparse Reconstruction of Multi-Dimensional Spectroscopic Imaging in Human Brain in vivo
by Brian L. Burns, Neil E. Wilson and M. Albert Thomas
Algorithms 2014, 7(3), 276-294; https://doi.org/10.3390/a7030276 - 26 Jun 2014
Cited by 17 | Viewed by 5720
Abstract
Four-dimensional (4D) Magnetic Resonance Spectroscopic Imaging (MRSI) data combining 2 spatial and 2 spectral dimensions provides valuable biochemical information in vivo; however, its 20–40 min acquisition time is too long to be used for a clinical protocol. Data acquisition can be accelerated by [...] Read more.
Four-dimensional (4D) Magnetic Resonance Spectroscopic Imaging (MRSI) data combining 2 spatial and 2 spectral dimensions provides valuable biochemical information in vivo; however, its 20–40 min acquisition time is too long to be used for a clinical protocol. Data acquisition can be accelerated by non-uniformly under-sampling (NUS) the ky− t1 plane, but this causes artifacts in the spatial-spectral domain that must be removed by non-linear, iterative reconstruction. Previous work has demonstrated the feasibility of accelerating 4D MRSI data acquisition through NUS and iterative reconstruction using Compressed Sensing (CS), Total Variation (TV), and Maximum Entropy (MaxEnt) reconstruction. Group Sparse (GS) reconstruction is a variant of CS that exploits the structural sparsity of transform coefficients to achieve higher acceleration factors than traditional CS. In this article, we derive a solution to the GS reconstruction problem within the Split Bregman iterative framework that uses arbitrary transform grouping patterns of overlapping or non-overlapping groups. The 4D Echo-Planar Correlated Spectroscopic Imaging (EP-COSI) gray matter brain phantom and in vivo brain data are retrospectively under-sampled 2×, 4×, 6×, 8×, and 10___ and reconstructed using CS, TV, MaxEnt, and GS with overlapping or non-overlapping groups. Results show that GS reconstruction with overlapping groups outperformed the other reconstruction methods at each NUS rate for both phantom and in vivo data. These results can potentially reduce the scan time of a 4D EP-COSI brain scan from 40 min to under 5 min in vivo. Full article
(This article belongs to the Special Issue Data Compression for the Life Sciences)
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