SHERPA: an image segmentation and outline feature extraction tool for diatoms and other objects.
SHERPA: an image segmentation and outline feature extraction tool for diatoms and other objects.
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DOI：
10.1186/1471-2105-15-218
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发表时间：
2014-06-25
影响因子：
3
通讯作者：
Beszteri B
中科院分区：
文献类型：
--
作者：
Kloster M;Kauer G;Beszteri B
硅藻硅藻壳的光学显微镜分析广泛用于基础和应用研究，特别是分类学，形态测量学，水质监测和古环境研究。在这些应用中，通常需要识别和/或测量大量硅藻壳。虽然在这些应用中需要自动化，并且先前已经开发了支持这些任务的图像处理和分析方法，但它们在硅藻分析中并没有普及。虽然有各种各样的图像分割、硅藻识别和特征提取方法的方法学报告，但没有一个单一的实施方案将这些方法的子集组合成一个容易适用的工作流程，供专家使用。新开发的工具SHERPA提供了一个多功能的图像处理工作流程，专注于识别和测量物体轮廓，处理从图像分割到物体识别到特征提取的所有步骤，并提供交互式功能以审查和修改结果。特别注意的是易于使用，适用于广泛的数据和问题，并支持高通量分析，最小的人工干预。通过对来自不同来源和不同成分的几个硅藻数据集的测试，SHERPA证明了其成功分析描绘广泛物种的大量硅藻显微照片的能力。SHERPA的独特之处在于结合了以下特点：应用多种分割方法，并为每个对象选择一种最佳分割方法;根据模板库的轮廓匹配识别感兴趣的形状;对所得轮廓进行质量评分和排名，支持快速质量检查;提取广泛用于硅藻研究和其他地方的轮廓形状描述符;最小化对手动质量控制和校正的需要，但能够实现手动质量控制和校正。虽然SHERPA主要用于分析来自自动显微镜的硅藻瓣膜图像，但它也可用于其他对象检测，分割和基于轮廓的识别问题。
Light microscopic analysis of diatom frustules is widely used both in basic and applied research, notably taxonomy, morphometrics, water quality monitoring and paleo-environmental studies. In these applications, usually large numbers of frustules need to be identified and/or measured. Although there is a need for automation in these applications, and image processing and analysis methods supporting these tasks have previously been developed, they did not become widespread in diatom analysis. While methodological reports for a wide variety of methods for image segmentation, diatom identification and feature extraction are available, no single implementation combining a subset of these into a readily applicable workflow accessible to diatomists exists. The newly developed tool SHERPA offers a versatile image processing workflow focused on the identification and measurement of object outlines, handling all steps from image segmentation over object identification to feature extraction, and providing interactive functions for reviewing and revising results. Special attention was given to ease of use, applicability to a broad range of data and problems, and supporting high throughput analyses with minimal manual intervention. Tested with several diatom datasets from different sources and of various compositions, SHERPA proved its ability to successfully analyze large amounts of diatom micrographs depicting a broad range of species. SHERPA is unique in combining the following features: application of multiple segmentation methods and selection of the one giving the best result for each individual object; identification of shapes of interest based on outline matching against a template library; quality scoring and ranking of resulting outlines supporting quick quality checking; extraction of a wide range of outline shape descriptors widely used in diatom studies and elsewhere; minimizing the need for, but enabling manual quality control and corrections. Although primarily developed for analyzing images of diatom valves originating from automated microscopy, SHERPA can also be useful for other object detection, segmentation and outline-based identification problems.
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影响因子：
3.7
作者：
Abarca N;Jahn R;Zimmermann J;Enke N
通讯作者：
Enke N
DOI：
10.1109/tit.1962.1057692
发表时间：
1962-01-01
期刊：
IRE TRANSACTIONS ON INFORMATION THEORY
影响因子：
--
作者：
HU, M
通讯作者：
HU, M
影响因子：
4.5
作者：
Jewson, David H.;Granin, Nick G.;Gnatovsky, Ruslan Yu.
通讯作者：
Gnatovsky, Ruslan Yu.
影响因子：
2.5
作者：
Kermarrec, Lenaig;Bouchez, Agnes;Humbert, Jean-Francois
通讯作者：
Humbert, Jean-Francois
影响因子：
5.3
作者：
Cortese, G.;Gersonde, R.
通讯作者：
Gersonde, R.