Access the full text.
Sign up today, get DeepDyve free for 14 days.
M. Bern, DavidE Goldberg, R. Stevens, P. Kuhn (2004)
Automatic classification of protein crystallization images using a curve‐tracking algorithmJournal of Applied Crystallography, 37
V. Vapnik (2000)
The Nature of Statistical Learning Theory
B. Rupp
Acc. Chem. Res.
W. Zuk, K. Ward (1991)
Methods of analysis of protein crystal imagesJournal of Crystal Growth, 110
Robert Gustafson, Marty Gustafson, Brant White (1985)
United States patentGeothermics, 14
K. Saitoh, K. Kawabata, H. Asama, T. Mishima, M. Sugahara, M. Miyano (2005)
Evaluation of protein crystallization states based on texture information derived from greyscale images.Acta Crystallographica Section D-biological Crystallography, 61
P. Hart, R. Duda, D. Stork (1973)
Pattern Classification
V. Vapnik (1995)
The Nature of Statistical Learning
J. Wilson
Acta Cryst.
C.A. Cumbaa, A. Lauricella, N. Fehrman, C. Veatch, R. Collins, J. Luft, C. DeTitta, I. Jurisca
Automatic classification of sub‐microlitr protein‐crystallization trials in 1536‐well plates
B. Rupp (2003)
High-throughput crystallography at an affordable cost: the TB Structural Genomics Consortium Crystallization Facility.Accounts of chemical research, 36 3
E. Bodenstaff, F. Hoedemaeker, M. Kuil, Hans Vrind, Jan Abrahams (2002)
The prospects of protein nanocrystallography.Acta crystallographica. Section D, Biological crystallography, 58 Pt 11
M. Bern, D. Goldberg, R.C. Stevens, P. Kuhn
J. Appl. Cryst.
M. Sugahara, M. Miyano (2002)
[Development of high-throughput automatic protein crystallization and observation system].Tanpakushitsu kakusan koso. Protein, nucleic acid, enzyme, 47 8 Suppl
K. Saitoh, K. Kawabata, S. Kunimitsu, H. Asama, T. Mishima (2004)
Evaluation of protein crystallization states based on texture information2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 3
Purpose – 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. Design/methodology/approach – 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. Findings – 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. Originality/value – Contribution to automated evaluation process of the growth state of protein crystallization samples.
Sensor Review – Emerald Publishing
Published: Mar 28, 2008
Keywords: Crystallization; Image processing
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.