Standard image analysis is typically performed manually, which is often time-inefficient and in many cases imprecise. This is an issue particularly in the field of radiology where clinical decisions need to be made both quickly and accurately based on medical image analysis results. Therefore, automated image analysis has been a field of high interest over the past several decades. In this thesis, work is described in which different automated methods of image analysis are used to quantify automated image measurement precision, to segment the amount of calcium present in the coronary arteries, and to identify locations of asbestos burden along the pleural surface of the lung.In the first section a novel method of quantifying the dynamic measurement precision of an automated system measuring spherical, soot-producing fuel droplets undergoing combustion is presented. This automated measuring system utilizes least-squares circle/ellipse fitting to detect and measure the diameter of these droplets even when partially occluded by soot. From this, quality metrics such as gradient intensity and goodness of fit were identified, and synthetic image simulation was used to correlate these quality metrics with dynamic measurement precision. The final measurement system was able to achieve measurement precision of ± 0.2 pixels in cases of low occlusion. This work demonstrates that an automated image analysis system is capable of performing measurements with dynamic subpixel precision, far higher than what is possible with human annotations. In the second chapter, an automated system for detecting and quantifying coronary artery calcium (CAC) in cardiac computed tomography (CT) scans is described. This work utilized images from the Multi-Ethnic Study of Atherosclerosis (MESA) dataset along with radiologist- labeled pixel ground truths for each scan. This algorithm utilizes traditional computer vision techniques to achieve this goal. Within this, a heart segmentation was developed that enabled a general localization of the coronary arteries by mapping a probability map onto a heart surface mesh. This was used to identify candidate CAC plaques. Features were then extracted, and a random forest classifier was used to determine true and false CAC. This method achieved a Pearson correlation of 0.98, an average Dice score of 0.85, and a risk-category accuracy of 0.87. This work was able to achieve results similar to existing state-of-the-art methods. In the final chapter, an algorithm for detecting the presence of asbestos exposure in full- chest CT scans is presented. This work focused on patients that live in Libby, Montana where a significant percent of the population has been exposed to asbestos originating from nearby vermiculite mines. Asbestos leads to a multitude of presentations on the lung surface including the thickening of the pleural membrane and manifestation of plaques. Specifically, in Libby cases, patients exhibit a specific type of pleural thickening, like lamellar pleural thickening, on chest CT scans that is difficult to detect by radiologist. Therefore, to detect this thickening and other disease conditions, the lung was segmented, and its surface was sampled in an orthogonal fashion to obtain subpixel intensity profiles along the edge of the lung. Features from these profiles were then used to identify regions of interest where there was likely asbestos exposure. This method was evaluated on healthy and diseased Libby cases as well as an external dataset [Lung Image Database Consortium (LIDC)]. The fraction of the lung surface identified as diseased by the algorithm was significantly higher in the diseased cases when compared to the healthy and LIDC cases. This showed that the methods developed for identifying pleural thickening in Libby cases was successfully achieved.