  We consider the problem of finding an accurate representation of neuron
shapes, extracting sub-cellular features, and classifying neurons based on
neuron shapes. In neuroscience research, the skeleton representation is often
used as a compact and abstract representation of neuron shapes. However,
existing methods are limited to getting and analyzing "curve" skeletons which
can only be applied for tubular shapes. This paper presents a 3D neuron
morphology analysis method for more general and complex neuron shapes. First,
we introduce the concept of skeleton mesh to represent general neuron shapes
and propose a novel method for computing mesh representations from 3D surface
point clouds. A skeleton graph is then obtained from skeleton mesh and is used
to extract sub-cellular features. Finally, an unsupervised learning method is
used to embed the skeleton graph for neuron classification. Extensive
experiment results are provided and demonstrate the robustness of our method to
analyze neuron morphology.
