LEADER 01749nam  2200397z- 450 
001 9910346767303321
005 20210211
010   $a1000060221
035   $a(CKB)4920000000100850
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/54603
035   $a(oapen)doab54603
035   $a(EXLCZ)994920000000100850
100   $a20202102d2017      |y         0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aNew Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
210   $cKIT Scientific Publishing$d2017
215   $a1 online resource (XII, 243 p. p.)
311 08$a3-7315-0590-8 
330   $aMultidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images.
610   $a3D Bildanalyse
610   $aAlgorithmen
610   $aAlgorithms
610   $aData Mining
610   $aDevelopmental Biology
610   $aEntwicklungsbiologie
610   $aSoftware
610   $aSoftware3D Image Analysis
700   $aStegmaier$b Johannes$4auth$01323753
906   $aBOOK
912   $a9910346767303321
996   $aNew Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty$93035813
997   $aUNINA