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- W2342970721 abstract "This dissertation explores the concept and provides a critical evaluation of accumulated error in the segmentation of digital medical image data, typified by Computed Tomography (CT). Error is inherent in the digital representation of any image data.It was hypothesized that the level of error in the human perception of objects in digital medical image data is significant, and greater than widely believed. This dissertation investigated both the error and methods for reducing it by exploring the intra-observer and inter-observer segmentation variation in both manual and computer enhanced segmentation tasks.In the process of exploring the concept of accumulated error, the dissertation research provided a number of insights. First, it was noted that digital x-ray data acquisition devices are not generally designed to ensure maximum geometric data accuracy, rather, they are designed in an attempt to ensure maximum sensitivity of detection. Second, due to the effect of acquisition device design, subtle errors and artifacts can be introduced into any data set, the most important of which appears to be motion artifact. Third, studies obtained for surgical imaging or biomechanical analysis purposes should include a calibration standard with known dimensions.The dissertation experiments resulted in a number of findings and conclusions. It was noted that intra-observer and inter-observer variation in the segmentation of object areas may vary, on average, between 5% and 10% of the mean segmented area for the former, and between 10% and 20% for the latter, although the ranges may fluctuate depending upon the areas segmented and segmentation conditions. The research also showed that a consistent definition of an object's boundary can be obtained by computer enhancing a manually entered template. This enhanced manual segmentation significantly reduced the inherent variation seen in manual data segmentation, often by as much as 50%. It was concluded that the data acquisition and analysis protocol developed herein assures one of obtaining relatively accurate geometric data from computed tomographic data sets." @default.
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- W2342970721 title "A characterization of error in the segmentation of computed tomographic data and its effect on surgical imaging" @default.
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