Sentence examples for approaches for background from inspiring English sources
Many segmentation approaches for background subtraction have been proposed over the past decades [5 9].
In particular, the approaches for background activity assessment are variable, although background assessment is essential for the evaluation of TBR values as well as quantification of biological tumor volume.
Hence, we intended to elucidate the effects of different approaches for background activity assessment and to evaluate simple and clinically applicable methods of background activity assessment for 18F-FET PET imaging regarding their inter- and intra-reader variability in the light of an emphasized comprehensive standardization of amino acid PET.
Harmonizing the tools and models of toxicological risk assessment with those of epidemiological risk assessment, and reconciling their data and results, should facilitate the development of better approaches for background and variability descriptions in NexGen human health risk assessments.
Our proposed approach for background modelling for videos has some similarities with the NL-means algorithm described in [14].
Thus, the above-mentioned approach for background generation using recent value (i.e., distorted recent value) also loses its meaning.
Nonetheless, the interfering factor can considerably be reduced as highlighted using the crescent-shaped VOI approach for background activity assessment.
In the context of the anticipated standardized technical guidelines for glioma PET imaging procedures and recommendations by the EANM, EANO, and RANO [1], the use of a standardized approach for background activity assessment might be an important methodological landmark.
An efficient approach for background initialization and robust method of edge matching is presented, to effectively reduce the risk of false alarm due to illumination change and camera motion while maintaining the high sensitivity to the presence of moving object.
Gene expression estimates were calculated using the library GC-RMA, employing the empirical Bayes approach for background correction followed by quantile normalization.
Expression estimates of probesets were obtained by GC-robust multi-array (GCRMA) analysis, employing the empirical Bayes approach for background adjustment, followed by quantile normalization and summarization [ 11].
