  Individual locations of many neuronal cell bodies (>10^4) are needed to
enable statistically significant measurements of spatial organization within
the brain such as nearest-neighbor and microcolumnarity measurements. In this
paper, we introduce an Automated Neuron Recognition Algorithm (ANRA) which
obtains the (x,y) location of individual neurons within digitized images of
Nissl-stained, 30 micron thick, frozen sections of the cerebral cortex of the
Rhesus monkey. Identification of neurons within such Nissl-stained sections is
inherently difficult due to the variability in neuron staining, the overlap of
neurons, the presence of partial or damaged neurons at tissue surfaces, and the
presence of non-neuron objects, such as glial cells, blood vessels, and random
artifacts. To overcome these challenges and identify neurons, ANRA applies a
combination of image segmentation and machine learning. The steps involve
active contour segmentation to find outlines of potential neuron cell bodies
followed by artificial neural network training using the segmentation
properties (size, optical density, gyration, etc.) to distinguish between
neuron and non-neuron segmentations. ANRA positively identifies 86[5]% neurons
with 15[8]% error (mean[st.dev.]) on a wide range of Nissl-stained images,
whereas semi-automatic methods obtain 80[7]%/17[12]%. A further advantage of
ANRA is that it affords an unlimited increase in speed from semi-automatic
methods, and is computationally efficient, with the ability to recognize ~100
neurons per minute using a standard personal computer. ANRA is amenable to
analysis of huge photo-montages of Nissl-stained tissue, thereby opening the
door to fast, efficient and quantitative analysis of vast stores of archival
material that exist in laboratories and research collections around the world.
