A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus.

ABSTRACT: 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 a

SUBMITTER: David C 

PROVIDER: S-EPMC11630814 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature