A multichannel order-statistic technique for cDNA microarray image processing.
This paper introduces an automated image processing procedure capable of processing complementary deoxyribonucleic acid (cDNA) microarray images. Microarray data is contaminated by noise and suffers from broken edges and visual artifacts. Without the utilization of a filter, subsequent tasks such as spot identification and gene expression determination cannot be completed. By employing, in a unique cascade processing cycle, nonlinear filtering solutions based on robust order statistics, the procedure: 1) removes both background and high-frequency corrupting noise and 2) correctly identifies edges and spots in cDNA microarray data. The proposed solution operates directly on the microarray data, does not rely on explicit data normalization or spot separation preprocessing, and operates in a robust manner without using heuristically determined design parameters. Other routine microarray processing operations such as shape manipulations and grid adjustments can be used in conjunction with the developed solution in the processing pipeline. Experimentation reported in this paper indicates that the proposed solution yields excellent performance by removing noise and enhancing spot location determination.
Lukac
Rastislav
R
Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada. lukacr@ieee.org
Plataniotis
Konstantinos N
KN
Smolka
Bogdan
B
Venetsanopoulos
Anastasios N
AN
D000465:Algorithms; D016477:Artifacts; D020869:Gene Expression Profiling / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D013269:Stochastic Processes