An image segmentation method based on the spatial correlation coefficient of Local Moran’s I

Abstract

AbstractUnsupervised 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 (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 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. 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 nose conditions.Significance statementMost images of natural objects are noisy, especially when captured at the resolution limit of the optical devices. The simplest way of differentiating between pixels of objects and noise is to examine the neighboring pixels. Statistical evaluation of local spatial correlation highlights assemblies of non-random bright pixels representing tiny biological entities, e.g. potassium channel clusters. Local Moran’s I allows detecting borders of fuzzy objects therefore it can be a basis of a user independent image segmentation method. This straightforward method outperforms threshold based segmentation methods and does not require a tedious training of artificial intelligence. The method could identify a previously unknown association of specialized presynaptic terminal type with postsynaptic ion channel clusters.