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Crystalline object evaluation by image processing

Kuniaki Kawabata (RIKEN, Hirosawa, Wako, Japan)
Mutsunori Takahashi (Saitama University, Sakura, Saitama, Japan)
Kanako Saitoh (Saitama University, Sakura, Saitama, Japan)
Mitsuaki Sugahara (RIKEN, Spring‐8 Center, Sayo‐cho, Sayo, Japan)
Hajime Asama (The University of Tokyo, Kashiwanoha, Kashiwa, Japan)
Taketoshi Mishima (Saitama University, Sakura, Saitama, Japan)
Masashi Miyano (RIKEN, Spring‐8 Center, Sayo‐cho, Sayo, Japan)

Sensor Review

ISSN: 0260-2288

Article publication date: 28 March 2008




The purpose of this paper is to propose a state discrimination for crystallization samples (droplets), the purpose of which is to discriminate between diffractable extracts (crystal) and other objects.


The line feature from the image of the protein droplet was extracted and the state discriminated using a classifier based on line features. A support vector machine is used as the classifier.


In order to verify the performance of the proposed method, the growth state was discriminated experimentally using the images taken by TERA, an automated crystallization system. The correction ratio was determined to exceed 80 percent.


Contribution to automated evaluation process of the growth state of protein crystallization samples.



Kawabata, K., Takahashi, M., Saitoh, K., Sugahara, M., Asama, H., Mishima, T. and Miyano, M. (2008), "Crystalline object evaluation by image processing", Sensor Review, Vol. 28 No. 2, pp. 143-149.



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Copyright © 2008, Emerald Group Publishing Limited

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