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The purpose of this study was to compare the ability of several texture analysis parameters to
differentiate textured samples from a smooth control on images obtained with an Atomic Force
Microscope (AFM). Surface roughness plays a major role in the realm of material science, especially in
integrated electronic devices. As these devices become smaller and smaller, new materials with better
electrical properties are needed. New materials with smoother surface morphology have been found to
have superior electrical properties than their rougher counterparts. Therefore, in many cases surface
texture is indicative of the electrical properties that material will have. Physical vapor deposition
techniques such as Jet Vapor Deposition and Molecular Beam Epitaxy are being utilized to synthesize
these materials as they have been found to create pure and uniform thin layers. For the current study,
growth parameters were varied to produce a spectrum of textured samples. The focus of this study was
the image processing techniques associated with quantifying surface texture. As a result of the limited
sample size, there was no attempt to draw conclusions about specimen processing methods. The
samples were imaged using an AFM in tapping mode. In the process of collecting images, it was
discovered that roughness data was much better depicted in the microscope's "height" mode as opposed
to "equal area" mode. The AFM quantified the surface texture of each image by returning RMS
roughness and the first order histogram statistics of mean roughness, standard deviation, skewness, and
kurtosis. Color images from the AFM were then processed on an off line computer running NIH ImageJ
with an image texture plug in. This plug in produced another set of first order statistics computed from
each images' histogram as well as second order statistics computed from each images' cooccurrence
matrix. The second order statistics, which were originally proposed by Haralick, include contrast, angular
second moment, correlation, inverse difference moment, and entropy. These features were computed in
the 0°, 45°, 90°, and 135° directions. The findings of this study propose that the best combination of
quantitative texture parameters is standard deviation, 0° inverse difference moment, and 0° entropy, all of
which are obtained from the NIH ImageJ texture plug in.
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