GRAPHIE: graph based histology image explorer

BMC Bioinformatics

Research

Kun Huang¹  Hao Ding²  Raghu Machiraju²  Chao Wang³ 

[1] Biomedical Informatics Department, The Ohio State University, 43210, Columbus, OH, USA;Computer Science & Engineering Department, The Ohio State University, 43210, Columbus, OH, USA;Electrical & Computer Engineering, 43210, Columbus, OH, USA;

关键词: phenotypical analysis;    histology image exploration;    visual analytics tool;    graph visualization;   

DOI  :  10.1186/1471-2105-16-S11-S10

来源: Springer

【 摘 要 】

BackgroundHistology images comprise one of the important sources of knowledge for phenotyping studies in systems biology. However, the annotation and analyses of histological data have remained a manual, subjective and relatively low-throughput process.ResultsWe introduce Graph based Histology Image Explorer (GRAPHIE)-a visual analytics tool to explore, annotate and discover potential relationships in histology image collections within a biologically relevant context. The design of GRAPHIE is guided by domain experts' requirements and well-known InfoVis mantras. By representing each image with informative features and then subsequently visualizing the image collection with a graph, GRAPHIE allows users to effectively explore the image collection. The features were designed to capture localized morphological properties in the given tissue specimen. More importantly, users can perform feature selection in an interactive way to improve the visualization of the image collection and the overall annotation process. Finally, the annotation allows for a better prospective examination of datasets as demonstrated in the users study. Thus, our design of GRAPHIE allows for the users to navigate and explore large collections of histology image datasets.ConclusionsWe demonstrated the usefulness of our visual analytics approach through two case studies. Both of the cases showed efficient annotation and analysis of histology image collection.

【 授权许可】

Unknown   
© Ding et al. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.