Automated determination of parameters describing power spectra of micrograph images in electron microscopy
The current theory of image formation in electron microscopy has been semi-quantitatively successful in describing data. The theory involves parameters due to the transfer function of the microscope (defocus, spherical aberration constant, and amplitude constant ratio) as well as parameters used to...
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Veröffentlicht in: | Journal of structural biology 2003-10, Vol.144 (1), p.79-94 |
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creator | Huang, Zhong Baldwin, Philip R Mullapudi, Srinivas Penczek, Pawel A |
description | The current theory of image formation in electron microscopy has been semi-quantitatively successful in describing data. The theory involves parameters due to the transfer function of the microscope (defocus, spherical aberration constant, and amplitude constant ratio) as well as parameters used to describe the background and attenuation of the signal. We present empirical evidence that at least one of the features of this model has not been well characterized. Namely the spectrum of the noise background is not accurately described by a Gaussian and associated “
B-factor;” this becomes apparent when one studies high-quality far-from focus data. In order to have both our analysis and conclusions free from any innate bias, we have approached the questions by developing an automated fitting algorithm. The most important features of this routine, not currently found in the literature, are (i) a process for determining the cutoff for those frequencies below which observations and the currently adopted model are not in accord, (ii) a method for determining the resolution at which no more signal is expected to exist, and (iii) a parameter—with units of spatial frequency—that characterizes which frequencies mainly contribute to the signal. Whereas no general relation is seen to exist between either of these two quantities and the defocus, a simple empirical relationship approximately relates all three. |
doi_str_mv | 10.1016/j.jsb.2003.10.011 |
format | Article |
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B-factor;” this becomes apparent when one studies high-quality far-from focus data. In order to have both our analysis and conclusions free from any innate bias, we have approached the questions by developing an automated fitting algorithm. The most important features of this routine, not currently found in the literature, are (i) a process for determining the cutoff for those frequencies below which observations and the currently adopted model are not in accord, (ii) a method for determining the resolution at which no more signal is expected to exist, and (iii) a parameter—with units of spatial frequency—that characterizes which frequencies mainly contribute to the signal. Whereas no general relation is seen to exist between either of these two quantities and the defocus, a simple empirical relationship approximately relates all three.</description><identifier>ISSN: 1047-8477</identifier><identifier>EISSN: 1095-8657</identifier><identifier>DOI: 10.1016/j.jsb.2003.10.011</identifier><identifier>PMID: 14643211</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Algorithms ; Biophysics - methods ; Chaperonin 60 - chemistry ; Electron microscopy ; Fourier Analysis ; Hemocyanins - chemistry ; Image Processing, Computer-Assisted - methods ; Microscopy, Electron - methods ; Models, Statistical ; Normal Distribution ; Power spectrum ; Ribosomes - ultrastructure</subject><ispartof>Journal of structural biology, 2003-10, Vol.144 (1), p.79-94</ispartof><rights>2003 Elsevier Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c349t-89dfbd26bc2b08c56fb9b48abaed2f6fc8a0c71994feafa14950b9e9f227a1ae3</citedby><cites>FETCH-LOGICAL-c349t-89dfbd26bc2b08c56fb9b48abaed2f6fc8a0c71994feafa14950b9e9f227a1ae3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1047847703002247$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/14643211$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Huang, Zhong</creatorcontrib><creatorcontrib>Baldwin, Philip R</creatorcontrib><creatorcontrib>Mullapudi, Srinivas</creatorcontrib><creatorcontrib>Penczek, Pawel A</creatorcontrib><title>Automated determination of parameters describing power spectra of micrograph images in electron microscopy</title><title>Journal of structural biology</title><addtitle>J Struct Biol</addtitle><description>The current theory of image formation in electron microscopy has been semi-quantitatively successful in describing data. The theory involves parameters due to the transfer function of the microscope (defocus, spherical aberration constant, and amplitude constant ratio) as well as parameters used to describe the background and attenuation of the signal. We present empirical evidence that at least one of the features of this model has not been well characterized. Namely the spectrum of the noise background is not accurately described by a Gaussian and associated “
B-factor;” this becomes apparent when one studies high-quality far-from focus data. In order to have both our analysis and conclusions free from any innate bias, we have approached the questions by developing an automated fitting algorithm. The most important features of this routine, not currently found in the literature, are (i) a process for determining the cutoff for those frequencies below which observations and the currently adopted model are not in accord, (ii) a method for determining the resolution at which no more signal is expected to exist, and (iii) a parameter—with units of spatial frequency—that characterizes which frequencies mainly contribute to the signal. 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The theory involves parameters due to the transfer function of the microscope (defocus, spherical aberration constant, and amplitude constant ratio) as well as parameters used to describe the background and attenuation of the signal. We present empirical evidence that at least one of the features of this model has not been well characterized. Namely the spectrum of the noise background is not accurately described by a Gaussian and associated “
B-factor;” this becomes apparent when one studies high-quality far-from focus data. In order to have both our analysis and conclusions free from any innate bias, we have approached the questions by developing an automated fitting algorithm. The most important features of this routine, not currently found in the literature, are (i) a process for determining the cutoff for those frequencies below which observations and the currently adopted model are not in accord, (ii) a method for determining the resolution at which no more signal is expected to exist, and (iii) a parameter—with units of spatial frequency—that characterizes which frequencies mainly contribute to the signal. Whereas no general relation is seen to exist between either of these two quantities and the defocus, a simple empirical relationship approximately relates all three.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>14643211</pmid><doi>10.1016/j.jsb.2003.10.011</doi><tpages>16</tpages></addata></record> |
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subjects | Algorithms Biophysics - methods Chaperonin 60 - chemistry Electron microscopy Fourier Analysis Hemocyanins - chemistry Image Processing, Computer-Assisted - methods Microscopy, Electron - methods Models, Statistical Normal Distribution Power spectrum Ribosomes - ultrastructure |
title | Automated determination of parameters describing power spectra of micrograph images in electron microscopy |
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