Image analysis as a tool for quantitative phycology: a computational approach to cyanobacterial taxa identification
Image analysis as a tool for quantitative phycology: a computational approach to cyanobacterial taxa identification
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图像分析作为定量藻类学的工具：蓝藻类群识别的计算方法
DOI：
10.1007/s102010070016
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发表时间：
2000
期刊：
影响因子：
1.6
通讯作者：
M. Kumagai
中科院分区：
文献类型：
--
作者：
Ross Walker;M. Kumagai
在接下来的工作中，我们将讨论图像处理和模式识别在定量心理学领域的应用。我们概述了图像处理的领域和审查以前发表的有关生理图像的图像分析的文献，特别是蓝藻图像处理。然后讨论用于处理图像和量化其中包含的数据的主要操作。为了证明图像处理对蓝藻分类的实用性，我们提出了一个图像分析系统的细节，用于自动检测和分类日本琵琶湖的几个蓝藻分类群。具体来说，我们首先针对微囊藻属进行检测和分类。我们随后扩展系统分类共六种蓝藻物种。分析包含上述物种和其他非目标物体混合的高分辨率显微镜图像，并从图像中删除任何检测到的物体以进行进一步分析。在图像增强之后，我们测量了物体的属性，并将它们与先前编译的物种特征数据库进行了比较。将一个物体分类为属于一个特定的类成员（例如，“微囊藻”，“a”）。smithii，“其他”等)使用参数统计方法执行。Leave-one-out分类结果表明，系统错误率约为3%。
In the following work we discuss the application of image processing and pattern recognition to the field of quantitative phycology. We overview the area of image processing and review previously published literature pertaining to the image analysis of phycological images and, in particular, cyanobacterial image processing. We then discuss the main operations used to process images and quantify data contained within them. To demonstrate the utility of image processing to cyanobacteria classification, we present details of an image analysis system for automatically detecting and classifying several cyanobacterial taxa of Lake Biwa, Japan. Specifically, we initially target the genusMicrocystisfor detection and classification from among several species ofAnabaena. We subsequently extend the system to classify a total of six cyanobacteria species. High-resolution microscope images containing a mix of the above species and other nontargeted objects are analyzed, and any detected objects are removed from the image for further analysis. Following image enhancement, we measure object properties and compare them to a previously compiled database of species characteristics. Classification of an object as belonging to a particular class membership (e.g., “Microcystis,”“A. smithii,”“Other,” etc.) is performed using parametric statistical methods. Leave-one-out classification results suggest a system error rate of approximately 3%.