Image processing for novel and efficient quantification of neuronal transport
- Biological research is highly visual, as advances in imaging technologies over the last few decades have enabled biologists to discern increasingly specific structures using fluorescence microscopy. However, two obstacles limit the amount of information that can be extracted from images. The first is the diffraction limit, the fundamental limit that dictates how small of an object can be resolved by light waves. The second challenge is the necessity to efficiently and objectively process the large numbers of images that today's technology can acquire. Current research in the fields of image processing and computer vision is not directly applicable to biological images, because it focuses primarily on extracting information from natural images with strong features. This dissertation leverages some of these engineering techniques to create two tools to address the challenges of visual biological research in the context of neuronal transport. These tools incorporate techniques from image processing and machine learning to effectively extract information about various molecules associated with neuronal transport, including structures ten times smaller than the diffraction limit. With the computational methods described in this dissertation, biologists can quantify various biological phenomena rapidly and robustly, and in turn, accelerate neuroscience research.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Stanford University, Department of Electrical Engineering.
|Shen, Kang, 1972-
|Shen, Kang, 1972-
|Statement of responsibility
|Submitted to the Department of Electrical Engineering.
|Thesis (Ph.D.)--Stanford University, 2016.
- © 2016 by Roshni Cooper
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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