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Image-based classification for automating protein crystal identification. (English) Zbl 1202.94090

Huang, De-Shuang (ed.) et al., Intelligent computing in signal processing and pattern recognition. International conference on intelligent computing, ICIC 2006, Kunming, China, August 16–19, 2006. Berlin: Springer (ISBN 3-540-37257-1/pbk). Lecture Notes in Control and Information Sciences 345, 932-937 (2006).
Summary: A technology for automatic evaluation of images from protein crystallization trials is presented in this paper. In order to minimize the interference posed by the environmental factors, the droplet is segmented from the entire image first. The algorithm selects different features, which are derived from the pixels within the droplet, and obtains a 16-dimensional feature vector which will then be fed to the classifier to make a classification. Each image is classified into one of the following classes: “Clear”, “Precipitate” and “Crystal”. We have achieved an accuracy rate of 84.8% with our algorithm.
For the entire collection see [Zbl 1103.94002].

MSC:

94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
92D10 Genetics and epigenetics
68T10 Pattern recognition, speech recognition
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