Visualization of parameter space for image analysis.

IEEE Trans Vis Comput Graph
Authors
Keywords
Abstract

Image analysis algorithms are often highly parameterized and much human input is needed to optimize parameter settings. This incurs a time cost of up to several days. We analyze and characterize the conventional parameter optimization process for image analysis and formulate user requirements. With this as input, we propose a change in paradigm by optimizing parameters based on parameter sampling and interactive visual exploration. To save time and reduce memory load, users are only involved in the first step--initialization of sampling--and the last step--visual analysis of output. This helps users to more thoroughly explore the parameter space and produce higher quality results. We describe a custom sampling plug-in we developed for CellProfiler--a popular biomedical image analysis framework. Our main focus is the development of an interactive visualization technique that enables users to analyze the relationships between sampled input parameters and corresponding output. We implemented this in a prototype called Paramorama. It provides users with a visual overview of parameters and their sampled values. User-defined areas of interest are presented in a structured way that includes image-based output and a novel layout algorithm. To find optimal parameter settings, users can tag high- and low-quality results to refine their search. We include two case studies to illustrate the utility of this approach.

Year of Publication
2011
Journal
IEEE Trans Vis Comput Graph
Volume
17
Issue
12
Pages
2402-11
Date Published
2011 Dec
ISSN
1941-0506
URL
DOI
10.1109/TVCG.2011.253
PubMed ID
22034361
PubMed Central ID
PMC3598613
Links
Grant list
R01 GM089652 / GM / NIGMS NIH HHS / United States
WT088908/Z/09/Z / Wellcome Trust / United Kingdom