Publication Date

2016

Document Type

Dissertation/Thesis

First Advisor

Zhou, Jie

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Computer Science

LCSH

Imaging systems--Image quality; Three-dimensional imaging; Automatic machinery--Computer programs

Abstract

This thesis explores computational methods for automatically detecting and quantifying synapses in complex 3D neuronal images. The resulting approach is a novel combination of traditional image processing, machine learning algorithms, and multi-channel comparison methods designed to overcome the unique challenges posed by these images. The methods investigated combine the strengths of each of these components in order to produce an overall method that is capable of fully detecting the synapses in large 3D confocal neuron images with minimal interaction. Human annotation of 3D neuron images remains prohibitively difficult and subjective, and computational analysis tools are highly desirable in the expanding field of 3D neuronal imaging. Validation techniques were also designed and implemented in order to test these methods for this thesis, including construction of a gold standard set of manually annotated synapse images. These are unique in their own right as there are currently no other data sets available for comparison. These methods were tested on multiple partial dendritic tree 3D images and a complete 3D dendrite with good outcomes. Quantitative validation was performed using the gold standard set to check the accuracy of synapse quantification, also with favorable results.

Comments

Advisors: Jie Zhou.||Committee members: Reva Freedman; Minmei Hou.||Includes bibliographical references.||Includes illustrations.

Extent

vi, 65 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

Download

Share

COinS