CAO, CHANGJIAN
(2019)
USING MACHINE LEARNING FOR FEATURE DETECTION IN TRANSMISSION ELECTRON MICROSCOPY.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
In situ testing performed in a transmission electron microscope (TEM) represents an im- portant technique for materials analysis. However, it produces a large amount of data in the form of images and video, which cannot typically be analyzed using traditional image- analysis algorithms. Therefore, a machine-learning approach is proposed to detect the shape of a body by recognizing and locating the border of the material. This supervised-learning al- gorithm is applied and a convolutional neural network is built to rapidly label all pixels. This network explores the relationship between small sub-images cropped from original images and their corresponding labels, and then it predicts the label when given new sub-images, thus generating a segmented image. In this project, the performance was assessed based on specificity, sensitivity, and accuracy of results. The overall accuracy of the present model is over 90%; however, the precision and recall rate are low due to high false-positive detection. This research suggests key factors for improving future machine-learning algorithms for TEM image analysis.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
18 June 2019 |
| Date Type: |
Publication |
| Defense Date: |
26 March 2019 |
| Approval Date: |
18 June 2019 |
| Submission Date: |
1 April 2019 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
42 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Mechanical Engineering |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Transmission electron microscopy, Convolutional neural networks, Supervised learning, Image processing. |
| Date Deposited: |
18 Jun 2019 17:48 |
| Last Modified: |
18 Jun 2021 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/36208 |
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