A computerized image analysis system for quantitative analysis of cells in histological brain sections

Abstract

We propose a reliable method for automatic counting of cells in brain sections labeled with different antibodies (against NeuN, parvalbumin, GABA and c-Fos) and in Nissl-staining. Images of stained sections are converted to binary images by thresholding. Clusters of ‘ON pixels’ (value of 1) corresponding to cell bodies are selected based on size. The parameters of the algorithm (intensity range and cluster-size) are adjusted for different methods of staining according to expert knowledge. The automatic cell counting method (ACCM) provides correct counting results, as demonstrated by a comparison of computational results with counts gained by human experimenters and with a commercially available image analysis system. On the basis of ACCM counts, small and perhaps physiologically relevant differences in the number of labeled cells can be revealed, as demonstrated here for the GABAergic system following electrical stimulation.