Visualisation for correlative multimodal imaging
Abstract
The field of visualisation deals with finding appropriate visual representations of data so people can effectively carry out tasks related to data exploration, analysis, or presentation using the power of the human visual perceptual system. In the context of biomedical imaging data, interactive visualisation techniques can be employed, for example, to visually explore data, as image processing quality assurance, or in publications to communicate findings. When dealing with correlative imaging, challenges arise in how to effectively convey the information from multiple sources. In particular, the information density leads to the need for a critical reflection on the visual design with respect to which parts of the data are important to show and at what level of importance they should be visualised. In this chapter, we describe several approaches to interactive imaging data visualisation in general, highlight several strategies for visualising correlative multimodal imaging data, and provide examples and practical recommendations.
N. Smit, K. Bühler, A. Vilanova, and M. Falk, "Visualisation for correlative multimodal imaging," in Imaging Modalities for Biological and Preclinical Research: A Compendium, Volume 2, IOP Publishing, 2021, p. III.4.e-1 to III.4.e-10. doi:10.1088/978-0-7503-3747-2ch28
[BibTeX]
The field of visualisation deals with finding appropriate visual representations of data so people can effectively carry out tasks related to data exploration, analysis, or presentation using the power of the human visual perceptual system. In the context of biomedical imaging data, interactive visualisation techniques can be employed, for example, to visually explore data, as image processing quality assurance, or in publications to communicate findings. When dealing with correlative imaging, challenges arise in how to effectively convey the information from multiple sources. In particular, the information density leads to the need for a critical reflection on the visual design with respect to which parts of the data are important to show and at what level of importance they should be visualised. In this chapter, we describe several approaches to interactive imaging data visualisation in general, highlight several strategies for visualising correlative multimodal imaging data, and provide examples and practical recommendations.
@incollection{Smit-2021-COMULIS,
author = {Smit, Noeska and Bühler, Katja and Vilanova, Anna and Falk, Martin},
title = {Visualisation for correlative multimodal imaging},
booktitle = {Imaging Modalities for Biological and Preclinical Research: A Compendium, Volume 2},
publisher = {IOP Publishing},
year = {2021},
series = {2053-2563},
type = {Book Chapter},
pages = {III.4.e-1 to III.4.e-10},
abstract = {In this chapter, we describe several approaches to interactive imaging data visualization in general, highlight several strategies for visualizing correlative multimodal imaging data, and provide examples and practical recommendations.},
url = {//dx.doi.org/10.1088/978-0-7503-3747-2ch28},
doi = {10.1088/978-0-7503-3747-2ch28},
isbn = {978-0-7503-3747-2},
thumbnails = "images/Smit-2021-COMULIS.PNG",
images = "images/Smit-2021-COMULIS.PNG",
project = "ttmedvis",
abstract = {The field of visualisation deals with finding appropriate visual representations of data so people can effectively carry out tasks related to data exploration, analysis, or presentation using the power of the human visual perceptual system. In the context of biomedical imaging data, interactive visualisation techniques can be employed, for example, to visually explore data, as image processing quality assurance, or in publications to communicate findings. When dealing with correlative imaging, challenges arise in how to effectively convey the information from multiple sources. In particular, the information density leads to the need for a critical reflection on the visual design with respect to which parts of the data are important to show and at what level of importance they should be visualised. In this chapter, we describe several approaches to interactive imaging data visualisation in general, highlight several strategies for visualising correlative multimodal imaging data, and provide examples and practical recommendations.}
}