Neuronal texture analysis in murine model of down’s syndrome

Abstract

An alteration of neuronal morphology is present in cognitive neurological diseases where learning or memory abilities are affected. The quantification of this alteration and its evolution by the study of microscopic images is essential. However, the use of advanced and automatic image processing techniques is currently very limited, focusing on the analysis of the morphology of isolated neurons. On this article we present a new methodology, based on texture analysis, to characterize the global distribution of different neural patterns in immunofluorescence images of brain tissue sections, where the neurons can be visualized as they are really distributed. We apply the technique to mice brain tissue section dividing them into two classes: Ts1Cje Down’s syndrome model and wild type, free of this neurodegenerative disease. Taking into account CA1 region of the hippocampus, we calculate and compare several state of the art texture descriptors that are subsequently classified using machine learning techniques. Achieving a 95% of accuracy, the assumption that texture characterization is relevant to quantify globally morphological alterations in the neurons, seems to be demonstrated.