Doctoral Dissertations

Mingzhong Li

Keywords and Phrases

Automated Monitoring; Biomedical; Computer Vision; Object Detection/Segmentation; Tracking

Abstract

"Studying the behavior patterns of biomedical objects helps scientists understand the underlying mechanisms. With computer vision techniques, automated monitoring can be implemented for efficient and effective analysis in biomedical studies. Promising applications have been carried out in various research topics, including insect group monitoring, malignant cell detection and segmentation, human organ segmentation and nano-particle tracking.

In general, applications of computer vision techniques in monitoring biomedical objects include the following stages: detection, segmentation and tracking. Challenges in each stage will potentially lead to unsatisfactory results of automated monitoring. These challenges include different foreground-background contrast, fast motion blur, clutter, object overlap and etc. In this thesis, we investigate the challenges in each stage, and we propose novel solutions with computer vision methods to overcome these challenges and help automatically monitor biomedical objects with high accuracy in different cases"--Abstract, page iii.

Advisor(s)

Yin, Zhaozheng

Committee Member(s)

Jiang, Wei
Silvestri, Simone
Lin, Dan
Qin, Ruwen

Department(s)

Computer Science

Degree Name

Ph. D. in Computer Science

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2016

Pagination

x, 69 pages

Note about bibliography

Includes bibliographic references (pages 64-68).

Rights

© 2016 Mingzhong Li, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Subject Headings

Biomedical materials
Computer vision
Pattern recognition systems

Thesis Number

T 10964

Electronic OCLC #

958280442

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