Date of Award


Degree Type


Degree Name

Departmental Honors


Electrical and Computer Engineering

First Advisor

Donald Cripps


Tissue engineering was once categorized as a subfield of biomaterials, but having grown in scope and importance, it can be considered as a field in its own right. Tissue engineering studies the mechanical properties of tissues and the applications that repair or replace portions of or whole tissues. The analyses of the mechanical properties of tissues can help in the diagnosis of tissue diseases and in the monitoring of the progress of tissue treatments and replacements.

In order to study the mechanical properties of tissues, it is often required to repeatedly compress and decompress the tissue. This deformation process helps simulating the body forces exerted on the tissue. The analyses of the displacement of particles of the tissues during deformation can be useful for the study of the mechanical properties of the tissues. An image that portrays the displacement of the particles during deformation is called displacement field. The phase data of MRI images of the cartilage during deformation are used to create displacement fields. In the process of creating the displacement fields, noise is produced disturbing the data. Consequently, the analyses of the mechanical properties of the tissues are not as accurate as desired.

The purpose of this project is to analyze the effectiveness of wavelet-based filters on eliminating the noise that is produced in the process of generating the displacement fields Wavelet filters decompose signals into wavelet components, suppress the components representing high-frequency using detail coefficient threshold algorithms, and reconstruct a de-noised signal from the components left. There are different variables in valved in the designing of wavelet-based filters. This project focuses an analyzing the effect of the variation of two design variables on the de-noising of displacement fields. These design variables are level of wavelet decomposition and detail coefficient threshold algorithms. This project aims to find an optimal combination of these two design variables. It also aims to compare wavelet-based filters to Fourier-based filters, which already have been tested for the de-noising of displacement fields. To test the effectiveness of the wavelet-based filters, Monte Carlo simulations are used on MATLAB.

Two variables are used on measuring the effectiveness of the wavelet filters: bias and precision error. Bias represents the average difference of pixel intensities between de-noised images and original images. Precision error represents the standard deviation of pixel intensities between de-noised images.


This work made publicly available electronically on January 3, 2011.