Visualization of parameter space for image analysis

© 2011 IEEE

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1998. - 17(2011), 12 vom: 01. Dez., Seite 2402-11
1. Verfasser: Pretorius, A Johannes (VerfasserIn)
Weitere Verfasser: Bray, Mark-Anthony P, Carpenter, Anne E, Ruddle, Roy A
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2011
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Androstadienes Chromones Morpholines 2-(4-morpholinyl)-8-phenyl-4H-1-benzopyran-4-one 31M2U1DVID Wortmannin XVA4O219QW
Beschreibung
Zusammenfassung:© 2011 IEEE
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
Beschreibung:Date Completed 24.02.2012
Date Revised 10.06.2024
published: Print
Citation Status MEDLINE
ISSN:1941-0506
DOI:10.1109/TVCG.2011.253