Neuron count in various brain structures is an important factor in
many neurobiological studies. We describe a machine vision system
which uses color images for the automated classification and counting
of neurons in tissue samples. Samples are sliced into registered
sections whose thickness is on the order of the diameter of a neuronal
nucleus. Sections are stained so that the spectral transmission
functions of the neuronal nuclei differ from the surrounding tissue.
Each section is imaged using a light microscope. A Bayesian classifier
is used for pixel labeling and a geometric analysis routine is employed
to segment neuron regions in each section. The 3-D tissue sample is
reconstructed using registered neuron regions from each section. An
object-oriented database management system provides an efficient
framework for cataloging neuron classes. Experimental results are
presented and compared with results obtained by a histologist.