Segmenting Compound Biomedical Figures into Their Constituent Panels.
Segmenting Compound Biomedical Figures into Their Constituent Panels.
复制标题
DOI：
10.1007/978-3-319-65813-1_20
复制
发表时间：
2017-09-01
期刊：
影响因子：
--
通讯作者：
Shatkay, Hagit
中科院分区：
文献类型：
--
作者：
Li, Pengyuan;Jiang, Xiangying;Shatkay, Hagit
生物医学出版物中的许多图是由多个面板组成的复合图。将这些图形分割成组成面板是收集生物医学文档中视觉信息的重要第一步。目前的数字分离方法主要是基于间隙检测，并遭受过分割和欠分割。本文提出了一种新的基于连通域分析的复合图形分割方法。为了克服现有方法通常表现出的缺点，我们开发了一个质量评估步骤，用于评估和修改分割。如果初始分割不准确，则提出两种方法来重新分割图像。实验结果表明，我们的方法与其他顶级方法的性能比较证明了我们的方法的有效性。
Many of the figures in biomedical publications are compound figures consisting of multiple panels. Segmenting such figures into constituent panels is an essential first step for harvesting the visual information within the biomedical documents. Current figure separation methods are based primarily on gap-detection and suffer from over- and under-segmentation. In this paper, we propose a new compound figure segmentation scheme 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 segmentations are inaccurate. Experiments and results comparing the performance of our method to that of other top methods demonstrate the effectiveness of our approach.