https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=1984VanHeel_MSA 2026-05-24T19:52:50Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=1984VanHeel_MSA&diff=1565&oldid=prev 2009-04-02T16:25:32Z

<p></p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> van Heel M (1984) Multivariate statistical classiWcation of noisy images<br /> (randomly oriented biological macromolecules). Ultramicroscopy<br /> 13:165–183<br /> <br /> == Abstract ==<br /> <br /> Multivariate Statistical Analysis (MSA) methods have recently been introduced for analyzing images of biological macromolecules [Van Heel and Frank, Ultramicroscopy 6 (1981) 187]. With these techniques, the significant characteristics of each molecular image can be expressed in merely 2 to 8 factorial coordinate values rather than in the typical 64 X 64 = 4096 pixel grey values that originally described the image. This very large reduction in total amount of data facilitates the understanding of the general behavior of a set of molecular images in terms of classes or of general trends in the data set. The (artificial) intelligence of the procedure, however, lies in the decision-making or classification phase. The theory and philosophy of multivariate statistical classification are reviewed using generalized metrics. Problem-dependent classification rationales are proposed. A set of computer-generated &quot;randomly oriented molecular images&quot; are used to test the classification schemes. This model experiment is a step towards 3D structure analysis of macromolecules based on large numbers of (noisy) electron microscopical images of randomly oriented biological macromolecules.<br /> <br /> == Keywords ==<br /> <br /> Image classification<br /> <br /> == Links ==<br /> <br /> Article http://www.ncbi.nlm.nih.gov/pubmed/6382731<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

WikiSysop