Röhlig, MartinLuboschik, MartinPrakasam, Ruby KalaStachs, OliverSchumann, HeidrunAnna Puig Puig and Tobias Isenberg2017-06-122017-06-122017978-3-03868-044-4https://doi.org/10.2312/eurp.20171176https://diglib.eg.org:443/handle/10.2312/eurp20171176Optical coherence tomography (OCT) enables noninvasive high-resolution imaging of the human retina and therefore, plays a fundamental role in detecting a wide range of ocular diseases. Yet, OCT data often vary in quality and show strong parameter dependencies. We propose a visual analysis approach to support users in understanding the influence of parameters on different aspects of the data. First, we outline the problem scope and derive requirements for a visual parameter analysis of OCT data. Second, we devise matched visual designs that disclose the impact of specific parameter values and the relationships between multiple parameter settings. With our systematic approach we aim at helping users in choosing suitable parameter settings and finding a balance between acquisition effort and data quality.Humancentered computingVisualizationVisualization application domainsVisual analytics10.2312/eurp.2017117689-91