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Pattern recognition software and techniques for biological image analysis.

Shamir L, Delaney JD, Orlov N, Eckley DM, Goldberg IG - PLoS Comput. Biol. (2010)

Bottom Line: This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed.Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging.We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Genetics, National Institute on Aging/National Institutes of Health, Baltimore, Maryland, United States of America.

ABSTRACT
The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.

Show MeSH
High-level architecture of bioimage analysis systems.
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pcbi-1000974-g001: High-level architecture of bioimage analysis systems.

Mentions: Although there are many examples of PR systems, the process can be summarized in several steps (Figure 1).


Pattern recognition software and techniques for biological image analysis.

Shamir L, Delaney JD, Orlov N, Eckley DM, Goldberg IG - PLoS Comput. Biol. (2010)

High-level architecture of bioimage analysis systems.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2991255&req=5

pcbi-1000974-g001: High-level architecture of bioimage analysis systems.
Mentions: Although there are many examples of PR systems, the process can be summarized in several steps (Figure 1).

Bottom Line: This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed.Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging.We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Genetics, National Institute on Aging/National Institutes of Health, Baltimore, Maryland, United States of America.

ABSTRACT
The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.

Show MeSH