@article {856,
	title = {An uncertainty-aware visual system for image pre-processing},
	journal = {Journal of Imaging},
	volume = {4},
	year = {2018},
	abstract = {Due to image reconstruction process of all image capturing methods, image data is inherently affected by uncertainty. This is caused by the underlying image reconstruction model, that is not capable to map all physical properties in its entirety. In order to be aware of these effects, image uncertainty needs to be quantified and propagated along the entire image processing pipeline. In classical image processing methodologies, pre-processing algorithms do not consider this information. Therefore, this paper presents an uncertainty-aware image pre-processing paradigm, that is aware of the input image{\textquoteright}s uncertainty and propagates it trough the entire pipeline. To accomplish this, we utilize rules for transformation and propagation of uncertainty to incorporate this additional information with a variety of operations. Resulting from this, we are able to adapt prominent image pre-processing algorithms such that they consider the input images uncertainty. Furthermore, we allow the composition of arbitrary image pre-processing pipelines and visually encode the accumulated uncertainty throughout this pipeline. The effectiveness of the demonstrated approach is shown by creating image pre-processing pipelines for a variety of real world datasets.},
	author = {Gillmann, Christina and Arbelaez, Pablo and Hernandez, Jose Tiberio and Hagen, Hans and Wischgoll, Thomas}
}
@conference {862,
	title = {Intuitive Error Space Exploration of Medical Image Data in Clinical Daily Routine},
	booktitle = {EuroVis 2017 Short Paper Track},
	year = {2017},
	abstract = {Medical image data can be affected by several image errors. These errors can lead to uncertain or wrong diagnosis in clinical daily routine. A large variety of image error metrics are available that target different aspects of image quality forming a highdimensional error space, which cannot be reviewed trivially. To solve this problem, this paper presents a novel error space exploration technique that is suitable for clinical daily routine. Therefore, the clinical workflow for reviewing medical data is extended by error space cluster information, that can be explored by user-defined selections. The presented tool was applied to two real-world datasets to show its effectiveness.},
	author = {Gillmann, Christina and Arbelaez, Pablo and Hernandez, Jose Tiberio and Hagen, Hans and Wischgoll, Thomas}
}
