Date of Award
Master of Science
Jacob R Rammer
Jacob R Rammer, Mohammad H Rahman, Jun Zhang
Bone Mineral Density, Hydroxyapatite Phantom, Image Segmentation, MicroCT, Python, Regionprops
The main objective of this study was to produce a Python script that would help facilitate the segmentation process of both bone and soft tissue. The proposed script, in tangent with ImageJ and Mimics, was successful in producing viable results when the bone and soft tissue sample was placed near hydroxyapatite (HA) phantoms during the image acquisition process. It was important to acquire both the HA phantoms and the sample within the same image sequence as the script functioned by analyzing the statistical distribution of the different HA regions to locate the most ideal thresholding ranges to determine the bone mineral density (BMD) percent composition. When the sample and HA phantoms were in the same set of images, they were both subject to the same type of noise and attenuation, thus allowing for better results to be produced. The script was successful in processing input images and was able to calculate the overall volume and surface area of both the bone and soft tissue, as well as determining the overall bone mineral density of bone. It was also attempted to process bone and soft tissue samples separate of the HA phantoms, but the results were inconclusive.
Lopez, Roberto, "Python-Based Analysis to Segment Bone and Soft Tissue in a Healing Callus" (2022). Theses and Dissertations. 3037.
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