New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
Computer Vision and Pattern Recognition
2016-08-31 v1
Abstract
Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced datasets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present thesis 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.
Cite
@article{arxiv.1608.08471,
title = {New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty},
author = {Johannes Stegmaier},
journal= {arXiv preprint arXiv:1608.08471},
year = {2016}
}
Comments
218 pages, 58 figures, PhD thesis, Department of Mechanical Engineering, Karlsruhe Institute of Technology, published online with KITopen (License: CC BY-SA 3.0, http://dx.doi.org/10.5445/IR/1000057821)