State of the Art in Information Extraction and Quantitative Analysis for Multimodality Biomolecular Imaging

Rapid advances in optical instrumentation, high-speed cameras, and fluorescent probes have spurred tremendous growth in the volume of biomolecular imaging data. Various optical imaging modalities are used for probing biological systems in vivo and in vitro. These include traditional two-dimensional...

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Bibliographic Details
Published in:Proceedings of the IEEE 2008-03, Vol.96 (3), p.512-531
Main Authors: Ahmed, Wamiq Manzoor, Leavesley, Silas J., Rajwa, Bartek, Ayyaz, Muhammad Naeem, Ghafoor, Arif, Robinson, J. Paul
Format: Article
Language:English
Subjects:
Biological
Biological system modeling
Biomedical optical imaging
Biomolecular imaging
Data mining
Extraction
High speed optical techniques
Image analysis
Imaging
In vitro
In vitro testing
In vivo
Information analysis
information extraction
Information retrieval
Instruments
Optical imaging
Quantitative analysis
quantitative imaging
Semantics
State of the art
Studies
Surgical implants
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Summary:Rapid advances in optical instrumentation, high-speed cameras, and fluorescent probes have spurred tremendous growth in the volume of biomolecular imaging data. Various optical imaging modalities are used for probing biological systems in vivo and in vitro. These include traditional two-dimensional imaging, three-dimensional confocal imaging, time-lapse imaging, and multispectral imaging. Many applications require a combination of these imaging modalities, which gives rise to huge data sets. However, lack of powerful information extraction and quantitative analysis tools poses a major hindrance to exploiting the full potential of the information content of these data. In particular, automated extraction of semantic information from multimodality imaging data, crucial for understanding biological processes, poses unique challenges. Information extraction from large sets of biomolecular imaging data requires modeling at multiple levels of detail to allow not only quantitative analysis but also interpretation and extraction of high-level semantic information. In this paper, we survey the state of the art in the area of information extraction and automated analysis tools for in vivo and in vitro biomolecular imaging. The modeling and knowledge extraction for these data require sophisticated image processing and machine learning techniques, as well as formalisms for information extraction and knowledge management. Development of such tools has the potential to significantly improve biological discovery and drug development processes.
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2007.913556