In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
will defend his dissertation
3-D Morphology Extraction of Neurons
Robust and accurate methods for the automatic extraction of neuronal morphology from 3D image stacks are necessary to quantify phenotypic changes of neurons. Recent developments in microscopy imaging have allowed the acquisition of large volumes of neuronal images, and manual reconstruction is infeasible since it requires an excessive amount of manual effort.. Imaging data from neuronal networks pose numerous challenges including: (i) low contrast, (ii) possibly high levels of noise, (iii) large difference in the size of the structures that need to be identified, and (iv) crossing and possibly gaps in the dendrites.
To address this critical need, this thesis presents new methods for the segmentation of dendrites and centerline extraction. The specific contributions include: (i) developed a framework for the generation of computational phantoms that can be used to evaluate segmentation algorithms of regular tubular structures; (ii) developed a segmentation algorithm able to segment regular dendrites; (iii) developed a segmentation algorithm able to identify irregular tubular structures (spines); and (iv) developed a centerline generation algorithm robust to the challenges outlined above. These novel methods have incorporated into the ORION software to facilitate rapid and objective processing of large-scale multispectral fluorescence images.
Date: Wednesday, Apr 29, 2015
Time: 2:30 PM
Place: HBS 350
Advisor: Prof. Ioannis A. Kakadiaris
Committee: Profs. C. Eick, I.A. Kakadiaris, D. Labate, F. Laezza, E. Papadakis, I. Pavlidis, S. Shah
Faculty, students, and the general public are invited.