| A central goal of modern neuroscience is to elucidate the computational principles and cellular mechanisms that underlie brain function, in both normal and diseased states. Notably, neuronal morphologies are broadly affected by age, genetic diseases such as Down's Syndrome, and degenerative diseases such as Alzheimer's. A major obstacle to this research is the lack of automated methods for three-dimensional reconstruction of cellular shape from imaging data to produce libraries of neurons with quantitative morphology suitable for computer simulation.;This dissertation presents a novel computational framework for automatic three-dimensional morphological reconstruction of nerve cells from optical images. More specifically, I propose: (i) a new algorithm for 3D noise removal in optical images; (ii) a novel method for detection of volumetric irregular tubular structures; and (iii) a robust algorithm for morphological reconstruction. My results are comparable with those obtained by human experts and outperform state-of-the-art computer algorithms. My novel methodology opens the way for the creation of neuron libraries and for guiding online functional imaging experiments in live neurons. |
