A comparative study of individual and ensemble majority vote cDNA microarray image segmentation schemes, originating from a spot-adjustable based restoration framework

Abstract

The aim of this study was to comparatively evaluate the performances of various segmentation algorithms, in conjunction with a noise reduction step, for gene expression levels intensity extraction in cDNA microarray images. Different segmentation algorithms, based on histogram and unsupervised classification methods, which have never been previously employed in microarray image analysis, were employed either individually or in ensemble majority vote structures for separating spot-images from background pixels. The performances of segmentation algorithms or ensemble structures were evaluated by assessing the validity and reproducibility of gene expression levels extraction in simulated and real cDNA microarray images. By processing high quality simulated images, the highest segmentation accuracy was achieved by an ensemble structure (Histogram Concavity, Gaussian Kernelized Fuzzy-C-Means, Seeded Region Growing). Optimum performance in terms of processing time and segmentation precision for low quality simulated and replicated real cDNA microarray images was attained by the Histogram Concavity algorithm.