Browsing by Author "Mitchell, Joseph Ross"

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    Focus+Context via Snaking Paths
    (2013-06-14) Packer, Jeffrey F.; Samavati, Faramarz F.; Mitchell, Joseph Ross
    Focus+context visualizations reveal specific structures in high detail while effectively depicting its surroundings, often relying on transitions between the two areas to provide context. We present an approach to generate focus+context visualizations depicting cylindrical structures along snaking paths that enables the structures themselves to become the transitions and focal areas, simultaneously. A method to automatically create a snaking path through space by applying a path finding algorithm is presented. A 3D curve is created based on the 2D snaking path. We describe a process to deform cylindrical structures in segmented volumetric models to match the curve and provide preliminary geometric models as templates for artists to build upon. Structures are discovered using our constrained volumetric sculpting method that enables removal of occluding material while leaving them intact. We find the resulting visualizations effectively mimic a set of motivating illustrations and discuss some limitations of the automatic approach.
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    Noise Reduction And Information Extraction Of Dual-Energy Computed Tomography Images
    (2016) Simon Maia, Rafael; Jacob, Christian; Mitchell, Joseph Ross; Boyd, Jeffrey; Cunningham, Ian; Costa Sousa, Mario; Frayne, Richard
    With every new generation of computed tomography machinery, big improvements in terms of image quality and acquisition speed were achieved. Nonetheless, a persistent feature of the CT remained: the single energy acquisition of images, which results in images where materials of similar density appear with similar intensity, generating an undesired degree of uncertainty that required other kinds of examinations to be solved. However, it has been known since its invention that the instantaneous acquisition of two or more energies would provide images with better tissue discrimination capabilities and reduce this problem. Nonetheless, it was only in the last 10 years that CT technology became advanced enough to simultaneously acquire images in dual-energy mode. However, it is necessary to keep the radiation dose to the patient equivalent to a single energy CT image, which results in images that are affected by noise and that need especial algorithms to improve the image quality. A particular feature that has also been know since the invention of dual energy CT is the characteristic negative correlation of its material density information discovered by Kalender et al. In this work we developed two algorithms that takes advantage of that discovery. We first created an algorithm using a joint anisotropic diffusion that reduced the amount of noise and improved image quality. Finally, we extended this first algorithm by using an adaptive Wiener filter that better approximated the true mean value of each region and drastically improved image quality even in images that were deeply affected by noise. The proposed techniques were tested in a quantitative way in simulated, real phantom and real patient images to show the improvement in image quality while preserving image information. Finally, we investigated these noise corrected images in the perspective of information extraction, using a modified multi-material decomposition algorithm to obtain a classification of pixel in term of tissue type.