Equilibrium modeling for 3D curvilinear structure tracking of confocal microscopy images

Neuron axon analysis through confocal microscopic image stack is dedicated in visualizing the geometrical features and topological characteristics of the 3D tubular biological objects, to ascertain the morphological properties and reconstruct the connectivity of neurons. This paper proposes a new curvilinear tracking algorithm which initializes a superellipsoid kernel into the tube by fitting the intensity energy distribution with multiple scales of steps and radii other than Hessian kernel. It is herein solved as an energy optimization in a graphical model with maximum likelihood, which preserves an equilibrium distribution across all the nodes with an attenuation penalty of orientation transition. Local potential energy diffusion of different axons is tracked by dynamic priority and pruning inference, to solve the cross-over problem. The centerline could be semi-automatically extracted following the selected initial point. Experimental results on 3D axon volumes verify that the proposed approach can handle complicated axon structures without elaborate segmentation.