2000年
Image analysis as a tool for quantitative phycology: A computational approach to cyanobacterial taxa identification
Limnology
- ,
- 巻
- 1
- 号
- 2
- 開始ページ
- 107
- 終了ページ
- 115
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1007/s102010070016
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 genus Microcystis for detection and classification from among several species of Anabaena. 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%. © The Japanese Society of Limnology 2000.
- ID情報
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- DOI : 10.1007/s102010070016
- ISSN : 1439-8621
- SCOPUS ID : 0000636149