Image analysis as a tool for quantitative phycology: a computational approach to cyanobacterial taxa identification |
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Authors: | Ross F Walker Michio Kumagai |
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Institution: | (1) Lake Biwa Research Institute, 1-10 Uchidehama, Otsu 520-0806, Japan Tel. +81-77-526-4690; Fax +81-77-526-4803 e-mail: walker@lbri.go.jp, JP |
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Abstract: | 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%.
Received: September 6, 1999 / Accepted: February 6, 2000 |
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Keywords: | Image processing Pattern recognition Phycology Cyanobacteria Classification |
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