Bogdan Smolka

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Vector processing operations use essential spectral and spatial information to remove noise and localize microarray spots. The proposed fully automated vector technique can be easily implemented in either hardware or software; and incorporated in any existing microarray image analysis and gene expression tool.
This paper presents a new cost-effective, adaptive multichannel filter taking advantage of switching schemes, robust order-statistic theory and approximation of the multivariate dispersion. Introducing the statistical control of the switching between the vector median and the identity operation, the developed filter enhances the detail-preserving capability(More)
In this paper, a class of weighted vector directional filters (WVDFs) based on the selection of the output sample from the multichannel input set is analyzed and optimized. The WVDF output minimizes the sum of weighted angular distances to other input samples from the filtering window. Dependent on the weighting coefficients, the class of the WVDFs can be(More)
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(More)
This paper introduces a class of nonlinear multichannel filters capable of removing impulsive noise in color images. The hereproposed generalized selection weighted vector filter class constitutes a powerful filtering framework for multichannel signal processing. Previously defined multichannel filters such as vector median filter, basic vector directional(More)