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

ABSTRACT: 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 enhanced 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 processi

SUBMITTER: Stegmaier J 

PROVIDER: S-EPMC5667823 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature