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Automatic Segmentation of Single Neurons Recorded by Wide-Field Imaging Using Frequency Domain Features and Clustering Tree


Abstract Recent new experiments showed that wide-field imaging at millimeter scale is capable of recording hundreds of neurons in behaving mice brain. Monitoring hundreds of individual neurons at a high frame rate provides a promising tool for discovering spatiotemporal features of large neural networks. However, processing the massive data sets is impossible without automated procedures. Thus, this thesis aims at developing a new tool to automatically segment and track individual neuron cells. The new method used in this study employs two major ideas including feature extraction based on power spectral density of single neuron temporal activity and clustering tree to separate overlapping cells. To address issues associated with high-resolution imag... (more)
Created Date 2016
Contributor Wu, Ruofan (Author) / Si, Jennie (Advisor) / Sadleir, Rosalind (Committee member) / Crook, Sharon (Committee member) / Arizona State University (Publisher)
Subject Neurosciences / Automatic Segmentation / Clustering tree / neural action potential / Power spectral density / single neuron / wide-field imaging
Type Masters Thesis
Extent 28 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Masters Thesis Electrical Engineering 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis