An image segmentation method based on the spatial correlation coefficient of Local Moran's I - identification of A-type potassium channel clusters in the thalamus.

Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here we propose a spatial autocorrelation method based on Local Morans I coefficient to differentiate signal, background and noise in any type of image. The method, originally described for geoinformatics, does not require a predefined intensity threshold or teaching algorithm for image segmentation and allows quantitative comparison of samples obtained in different conditions. It utilizes relative intensity as well as spatial information of neighboring elements to select spatially contiguous groups of pixels. We demonstrate that Morans method outperforms threshold-based method (TBM) in both artificially generated as well as in natural images especially when background noise is substantial. This superior performance can be attributed to the exclusion of false positive pixels resulting from isolated, high intensity pixels in high noise conditions. To test the methods power in real situation we used high power confocal images of the somatosensory thalamus immunostained for Kv4.2 and Kv4.3 (A-type) voltage gated potassium channels. Morans method identified high intensity Kv4.2 and Kv4.3 ion channel clusters in the thalamic neuropil. Spatial distribution of these clusters displayed strong correlation with large sensory axon terminals of subcortical origin. The unique association of the special presynaptic terminals and a postsynaptic voltage gated ion channel cluster was confirmed with electron microscopy. These data demonstrate that Morans method is a rapid, simple image segmentation method optimal for variable and high nose conditions.