Use Mean Shift to Track Neuronal Axons in 3D

Morphology is very important in help neuroscientists understand neuronal functions and connectivity of neurons. Using confocal microscopy researchers can acquire 3D images of neuronal axons in high resolution and study how axons innervate muscular fibers. To test different innervation models, researchers need to track every single axons and its branches in 3D. A robust segmentation and tracking method is needed to follow each axon in 3D. Challenges are that axons may appear touching each other in the image and make it difficult to segment. In addition, split and merge of axons require judicious image processing to correctly track axons in these cases. We present a 3-step segmentation and tracking algorithm to address these problems. Our proposed method includes nonlinear anisotropic diffusion for noise removal and edge enhancement, morphological operation for edge detection, and mean shift for tracking in three dimensions. The method can segment contacting objects and track the axons when they merge or split