Pattern recognition software and techniques for biological image analysis.

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 a...

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Autores principales:	Lior Shamir, John D Delaney, Nikita Orlov, D Mark Eckley, Ilya G Goldberg
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/26b76b6246044128bdc4bbe66adbb669
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spelling	oai:doaj.org-article:26b76b6246044128bdc4bbe66adbb6692021-11-18T05:51:53ZPattern recognition software and techniques for biological image analysis.1553-734X1553-735810.1371/journal.pcbi.1000974https://doaj.org/article/26b76b6246044128bdc4bbe66adbb6692010-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21124870/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The 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.Lior ShamirJohn D DelaneyNikita OrlovD Mark EckleyIlya G GoldbergPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 11, p e1000974 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Lior Shamir John D Delaney Nikita Orlov D Mark Eckley Ilya G Goldberg Pattern recognition software and techniques for biological image analysis.
description	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.
format	article
author	Lior Shamir John D Delaney Nikita Orlov D Mark Eckley Ilya G Goldberg
author_facet	Lior Shamir John D Delaney Nikita Orlov D Mark Eckley Ilya G Goldberg
author_sort	Lior Shamir
title	Pattern recognition software and techniques for biological image analysis.
title_short	Pattern recognition software and techniques for biological image analysis.
title_full	Pattern recognition software and techniques for biological image analysis.
title_fullStr	Pattern recognition software and techniques for biological image analysis.
title_full_unstemmed	Pattern recognition software and techniques for biological image analysis.
title_sort	pattern recognition software and techniques for biological image analysis.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/26b76b6246044128bdc4bbe66adbb669
work_keys_str_mv	AT liorshamir patternrecognitionsoftwareandtechniquesforbiologicalimageanalysis AT johnddelaney patternrecognitionsoftwareandtechniquesforbiologicalimageanalysis AT nikitaorlov patternrecognitionsoftwareandtechniquesforbiologicalimageanalysis AT dmarkeckley patternrecognitionsoftwareandtechniquesforbiologicalimageanalysis AT ilyaggoldberg patternrecognitionsoftwareandtechniquesforbiologicalimageanalysis
_version_	1718424715667701760

Pattern recognition software and techniques for biological image analysis.

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