CDNA microarray adaptive segmentation

Abstract

DNA microarray technology has enabled biologists to study all the genes within an entire organism to obtain a global view of genes׳ interaction and regulation. This technology has a great potential in obtaining a deep understanding of the functional organization of cells. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image׳s feature space in an unsupervised manner. Rather than using the raw microarray image, it proposes to produce filtered versions of the image data by applying nonlinear anisotropic diffusion so that the dynamic range of the image could be increased and, therefore, a better ability of signal extraction could be achieved. In this paper, a novel segmentation algorithm is developed. This algorithm is based on the Cellular Neural Network computational paradigm integrated with median and anisotropic diffusion filters. The AnaLogic CNN Simulation Toolbox for MATLAB (InstantVision Toolboxes for MATLAB) is used during the segmentation process. Quantitative comparisons among the proposed methods and GenePix® are carried out from both the objective and subjective points of view. It is shown that analogic algorithm integrated with Complex Diffusion filter is the best one to be applied to accomplish the segmentation.