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Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

Stegmaier, Johannes 1; Mikut, Ralf ORCID iD icon 1
1 Institut für Angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)


Many automatically analyzable scientific questions are well-posed and a variety of information about expected outcomes is available a priori. Although often neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this
prior knowledge. For instance, the performance of processing operators can be greatly inhanced by a more focused detection strategy and by direct information about the ambiguity inherent in the extracted data. We present a new concept that increases the result quality awareness of image analysis operators by estimating and distributing the degree of uncertainty involved in their output based on prior knowledge. This allows the use of simple processing operators that are suitable for analyzing large-scale spatiotemporal (3D+t) microscopy images without compromising result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it to enhance the result quality of various processing operators. These concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000076718
Veröffentlicht am 21.11.2017
DOI: 10.1371/journal.pone.0187535
Zitationen: 5
Zitationen: 9
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2017
Sprache Englisch
Identifikator ISSN: 1932-6203
KITopen-ID: 1000076718
HGF-Programm 47.01.02 (POF III, LK 01) Biol.Netzwerke u.Synth.Regulat. IAI
Erschienen in PLoS one
Verlag Public Library of Science (PLoS)
Band 12
Heft 11
Seiten Art.Nr.: e0187535
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 02.11.2017
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