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PDFOpen Access (License Unspecified), Open Access
Thesis
Classification of bone microstructures using PCA and 2-point statistics
Master of Science (M.S.), Drexel University
Sep 2012
DOI:
https://doi.org/10.17918/etd-4112
Abstract
A database framework that could automatically classify and organize characterized microstructure datasets based on a rigorous reduced variable representation of the microstructure rather than relying on ad-hoc selected metrics, can be a powerful tool in the field of materials science and engineering. Classification using lower order descriptors has been shown to improve the quantification of microstructures. Principal component analysis over a classification structure is suggested as an effective method for reduced order representation of a microstructure. In this paper, 2 point correlation functions and Principal Component Analysis (PCA) is used as an effective tool for classification of 3-D microstructures. [19]. The central hypothesis of this paper is that on the first 3 PC weights, bone microstructure would depict some classification based on the wedge location within the cross. Although there was no distinct classification based on wedge location, we see that microstructures clustered together in the low dimensional PCA representations actually look similar to each other i.e. visually, compared to the microstructures that are far apart from each other.
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Details
- Title
- Classification of bone microstructures using PCA and 2-point statistics
- Creators
- Alka Basnet - DU
- Contributors
- Surya Kalidindi (Advisor) - Drexel University (1970-)
- Awarding Institution
- Drexel University
- Degree Awarded
- Master of Science (M.S.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Resource Type
- Thesis
- Language
- English
- Academic Unit
- College of Engineering (1970-2026); Mechanical Engineering (and Mechanics) [Historical]; Drexel University
- Other Identifier
- 4112; 991014632716704721