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 Moran’s 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 Moran’s method outperforms threshold-based method 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
method’s 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
in mice. Moran’s 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 Moran’s
method is a rapid, simple image segmentation method optimal for variable and high
noise conditions.
