Masters Theses
Abstract
"Accurately detecting and classifying vesicle-plasma membrane fusion events in fluorescence microscopy, is of primary interest for studying biological activities in a close proximity to the plasma membrane. In this paper, we present a novel Gaussian mixture model for automated identification of vesicle-plasma membrane fusion and partial fusion events in total internal reflection fluorescence microscopy image sequences. Image patches of fusion event candidates are detected in individual images and linked over consecutive frames. A Gaussian mixture model is fit on each image patch of the patch sequence with outliers rejected for robust Gaussian fitting. The estimated parameters of Gaussian functions over time are catenated into feature vectors for classifier training. Applied on three challenging datasets, our method achieved competitive results on detecting and classifying fusion events compared with two state-of-the-art methods"--Abstract, page iii.
Advisor(s)
Yin, Zhaozheng
Committee Member(s)
Lin, Dan
Jiang, Wei
Department(s)
Computer Science
Degree Name
M.S. in Computer Science
Publisher
Missouri University of Science and Technology
Publication Date
Spring 2015
Pagination
viii, 15 pages
Note about bibliography
Includes bibliographical references (page 14).
Rights
© 2015 Haohan Li, All rights reserved.
Document Type
Thesis - Open Access
File Type
text
Language
English
Thesis Number
T 11200
Print OCLC #
1022846433
Electronic OCLC #
1014181779
Recommended Citation
Li, Haohan, "A Gaussian mixture model for automated vesicle fusion detection and classification" (2015). Masters Theses. 7708.
https://scholarsmine.mst.edu/masters_theses/7708
