Masters Theses

Author

Haohan Li

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 bibliographic references (page 14).

Rights

© 2015 Haohan Li, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 11200

Electronic OCLC #

1014181779

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