Neuron recognition by parallel Potts segmentation.

Identifying neurons and their spatial coordinates in images of the cerebral cortex is a necessary step in the quantitative analysis of spatial organization in the brain. This is especially important in the study of Alzheimer's disease (AD), in which spatial neuronal organization and relationships are highly disrupted because of neuronal loss. To automate neuron recognition by using high-resolution confocal microscope images from human brain tissue, we propose a recognition method based on statistical physics that consists of image preprocessing, parallel image segmentation, and cluster selection on the basis of shape, optical density, and size. We segment a preprocessed digital image into clusters by applying Monte Carlo simulations of a q-state inhomogeneous Potts model. We then select the range of Potts segmentation parameters to yield an ideal recognition of simplified objects in the test image. We apply our parallel segmentation method to control individuals and to AD patients and achieve recognition of 98% (for a control) and 93% (for an AD patient), with at most 3% false clusters.