Includes bibliographical references. / Three problems are addressed in this dissertation. They are intracranial volume extraction, noise suppression and automated segmentation of X-Ray Computerized Tomography (CT) images. The segmentation scheme is based on a Seeded Region Growing algorithm. The intracranial volume extraction is based on image symmetry and the noise suppression filter is based on the Gaussian nature of the tissue distribution. Both are essential in achieving good segmentation results. Simulated phantoms and real medical images were used in testing and development of the algorithms. The testing was done over a wide range of noise values, object sizes and mean object grey levels. All the methods were first implemented in two- and then three-dimensions. The 3-D implementation also included an investigation into volume formation and the advantages of 3-D processing. The results of the intracranial extraction showed that 9% of the data in the relevant grey level range consisted of unwanted scalp (The scalp is spatially not part of the intracranial volume, but has the same grey level values). This justified the extraction the intracranial volume for further processing. For phantom objects greater than 741.51mm³ (voxel resolution 0.48mm x 0.48mm x 2mm) and having a mean grey level distance of 10 from any other object, a maximum segmentation volume error of 15% was achieved.

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Geophysical tomography allows for the measurement of stress-induced density changes inside of a rock mass or sample by non-invasive means. Tomography is a non-destructive testing method by which sensors are placed around a sample and energy is introduced into the sample at one sensor while the other sensors receive the energy. This process is repeated around the sample to obtain the desired resolution. The received information is converted by a mathematical transform to obtain a tomogram. This tomogram shows a pixelated distribution of the density within the sample. Each pixel represents an average value at that point. The project discussed in this paper takes the principle of ultrasonic tomography and applies it to geomechanics. A new instrumentation system was designed to allow rapid data collection through varying sample geometries and rock types with a low initial investment. The system is composed of sensors, an ultrasonic pulser, a source switchbox, and analog to digital converters; it is tied together using a LabVIEW virtual instrument. LabVIEW is a graphical development environment for creating test, measurement, and other control applications. Using LabVIEW, virtual instruments (VIs) are created to control or measure a process. In this application LabVIEW was used to create a virtual instrument that was automated to collect the data required to construct a tomogram. Experiments were conducted to calibrate and validate the system for ultrasonic velocity determination and stress redistribution tomography. Calibration was conducted using polymethylmethacrylate (PMMA or Plexiglas) plates. Uniaxial loads were placed on limestone and sandstone samples. The stress-induced density contrasts were then imaged using the acquisition system. The resolution and accuracy of the system is described. The acquisition system presented is a low-cost solution to laboratory geophysical tomography. The ultimate goal of the project is to further the ability to non-invasively image relative stress redistribution in a rock mass, thereby improving the engineer's ability to predict failure. / Master of Science