title: An automatic block and spot indexing with k-nearest neighbors graph for microarray image analysis.
abstract: BACKGROUND
  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.
authors.affiliation: Department of Computer Science, Pusan National University, Jangjeon-dong, Keumjeong-gu, Korea. hyjung@pearl.cs.pusan.ac.kr
authors.forename: Ho-Youl
authors.initials: HY
authors.lastname: Jung
authors.forename: Hwan-Gue
authors.initials: HG
authors.lastname: Cho
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004247:DNA / Q000032:analysis* / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*
chemical_list: D004247:DNA