发表的生物医学图像的组合图像分割。
Compound image segmentation of published biomedical figures.
机构信息
Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.
出版信息
Bioinformatics. 2018 Apr 1;34(7):1192-1199. doi: 10.1093/bioinformatics/btx611.
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
SUPPLEMENTARY INFORMATION
Supplementary data are available online at Bioinformatics.
动机
图像在生物医学出版物中传达重要信息。因此，生物注释和生物数据库社区越来越有兴趣在出版物中存储图像，作为生物过程和实验结果的证据。然而，生物医学出版物中的许多图像都是由多个面板组成的复合图像，每个面板都可能传达不同类型的信息。将此类图像分割成组成面板是利用图像的重要第一步。
结果
在本文中，我们开发了一种新的复合图像分割系统 FigSplit，它基于连通分量分析。为了克服现有方法通常表现出的缺点，我们开发了一种质量评估步骤来评估和修改分割。如果初始分割不准确，则提出了两种重新分割图像的方法。实验结果表明，与其他方法相比，我们的方法是有效的。
可用性和实现
该系统可在以下网址公开使用：https://www.eecis.udel.edu/~compbio/FigSplit。如有需要，可提供代码。
联系人
补充信息
补充数据可在 Bioinformatics 在线获取。
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