Skip Navigation

This Article
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow FREE Full Text (Screen PDF)
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Jung, H.-Y.
Right arrow Articles by Cho, H.-G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Jung, H.-Y.
Right arrow Articles by Cho, H.-G.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Bioinformatics Vol. 18 no. 90002 2002
Pages S141-S151
© 2002 Oxford University Press

An automatic block and spot indexing with k-nearest neighbors graph for microarray image analysis

Ho-Youl Jung 1 and Hwan-Gue Cho 1

1 Department of Computer Science, Pusan National University, San-30, Jangjeon-dong, Keumjeong-gu, Pusan, 609-735, Korea

Received on April 8, 2002 ; accepted on June 15, 2002

Motivation: In this paper, we propose a fully automatic block and spot indexing algorithm for microarray image analysis. A microarray is a device which enables a parallel experiment of ten to hundreds of thousands of test genes in order to measure gene expression. Due to this huge size of experimental data, automated image analysis is gaining importance in microarray image processing systems. Currently, most of the automated microarray image processing systems require manual block indexing and, in some cases, spot indexing. If the microarray image is large and contains a lot of noise, it is very troublesome work. In this paper, we show it is possible to locate the addresses of blocks and spots by applying the Nearest Neighbors Graph Model. Also, we propose an analytic model for the feasibility of block addressing. Our analytic model is validated by a large body of experimental results.

Results: We demonstrate the features of automatic block detection, automatic spot addressing, and correction of the distortion and skewedness of each microarray image.

Contact: hyjung{at}pearl.cs.pusan.ac.kr


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
H. Peng
Bioimage informatics: a new area of engineering biology
Bioinformatics, September 1, 2008; 24(17): 1827 - 1836.
[Abstract] [Full Text] [PDF]

Home page
 Brief BioinformHome page
P. Larranaga, B. Calvo, R. Santana, C. Bielza, J. Galdiano, I. Inza, J. A. Lozano, R. Armananzas, G. Santafe, A. Perez, et al.
Machine learning in bioinformatics
Brief Bioinform, March 1, 2006; 7(1): 86 - 112.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
O. Demirkaya, M. H. Asyali, and M. M. Shoukri
Segmentation of cDNA Microarray Spots Using Markov Random Field Modeling
Bioinformatics, July 1, 2005; 21(13): 2994 - 3000.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
Q. Li, C. Fraley, R. E. Bumgarner, K. Y. Yeung, and A. E. Raftery
Donuts, scratches and blanks: robust model-based segmentation of microarray images
Bioinformatics, June 15, 2005; 21(12): 2875 - 2882.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.