3-D CENTERLINE EXTRACTION OF AXONS IN MICROSCOPIC STACKS FOR THE STUDY OF MOTOR NEURON BEHAVIOR IN DEVELOPING MUSCLES

This paper presents a new algorithm for extracting the centerlines of the axons from 3-D data stacks collected from a laser scanning confocal microscope. Recovery of neuronal structure from such datasets is critical for quantitatively addressing a range of basic biological questions such as the manner in which the branching pattern of motor neurons change during synapse elimination. The presence of artifacts in the cross-sectional images such as blurred boundaries and non-uniform intensities, makes the process of centerline extraction rather challenging. Although many methods exist in practice today, they are either error-prone or involve manual interaction to a large extent, when applied to this particular problem. We propose a robust probabilistic region growing algorithm to extract the centers from the datasets with minimal user interaction