Dataset Information
Compound image segmentation of published biomedical figures.
Ontology highlight
ABSTRACT: Motivation
Images convey essential information in biomedical publications. As such, there is a growing interest within the bio-curation and the bio-databases communities, to store images within publications as evidence for biomedical processes and for experimental results. However, many of the images in biomedical publications are compound images consisting of multiple panels, where each individual panel potentially conveys a different type of information. Segmenting such images into constituent panels is an essential first step toward utilizing images.Results
In this article, we develop a new compound image segmentation system, FigSplit, which is based on Connected Component Analysis. To overcome shortcomings typically manifested by existing methods, we develop a quality assessment step for evaluating and modifying segmentations. Two methods are proposed to re-segment the images if the initial segmentation is inaccurate. Experimental results show the effectiveness of our method compared with other methods.Availability and implementation
The system is publicly available for use at: https://www.eecis.udel.edu/~compbio/FigSplit. The code is available upon request.Contact
shatkay@udel.edu.Supplementary information
Supplementary data are available online at Bioinformatics.
SUBMITTER: Li P
PROVIDER: S-EPMC6030860 | biostudies-literature | 2018 Apr
REPOSITORIES: biostudies-literature
Publications
Compound image segmentation of published biomedical figures.
Bioinformatics (Oxford, England) 20180401 7
<h4>Motivation</h4>Images convey essential information in biomedical publications. As such, there is a growing interest within the bio-curation and the bio-databases communities, to store images within publications as evidence for biomedical processes and for experimental results. However, many of the images in biomedical publications are compound images consisting of multiple panels, where each individual panel potentially conveys a different type of information. Segmenting such images into con ...[more]
