Chest X-ray is one of the most commonly performed radiologic procedures for respiratory diseases. Digital tomosynthesis (DTS) provides volumetric anatomic information at lower cost and dose compared to computed tomography (CT). However, current DTS system provides insufficient patient positioning feedback and requires a large number of reconstructed slices in order to ensure imaging the entirety of targeted anatomy. We propose an anatomy registration prototype using measurements from RGB-D cameras to 1) assist acquisition workflow, 2) provide individual-specific anatomical information to improve tomosynthesis reconstruction. Our experiments show that anatomy registration can provide real-time feedback of patient 2D position and body thickness. Our reconstruction simulations show that the anatomy and body information can speed up DTS reconstruction, reduce the number of redundant tomosynthesis slices, and help reduce image interpretation time.
Tomography in the mobile setting has the potential to improve diagnostic outcomes by enabling 3D imaging at the patient’s bedside. Using component testing and system simulations, we demonstrate the potential for limited angle X-ray tomography on a mobile X-ray system. To enable mobile features such as low weight, size and power of system components, we have developed detector and patient anatomy tracking algorithms for accurately and automatically registering system geometry to patient anatomy during acquisition of individual projective-views along a tube-motion trajectory. We evaluate the effects of acquisition parameters and registration inaccuracy on image quality of reconstructed chest images using realistic X-ray simulation of an anthropomorphic numerical phantom of the thorax.
In this work, we introduce a method to segment hyperspectral images using a Chan-Vese framework. We utilize a modified l2 distance especially well-suited for hyperspectral classification problems. This distance considers spectral signal shape rather than illumination for the classification of objects. The practicality of multiple phase segmentation in this application is also demonstrated. We then use a high spatial resolution grayscale or color image and a high spectral, but low spatial resolution hyperspectral image to produce a fused segmentation result that is more accurate than segmentation on either image alone. Lastly, we show that the algorithm also gives a natural method for end member selection and apply this result to anomaly detection.
In this work, we survey image reconstruction methods for hyperspectral imagery. First, a review of image interpolation methods, both linear and nonlinear, is given. Second, image inpainting methods, especially from the variational perspective, are analyzed with respect to their suitability for hyperspectral inpainting. The ability to connect edges through occlusions and the structure of the space in which the hyperspectral data lies are especially considered when propagating data into unknown regions. Finally, a general method for adapting image reconstruction methods to the hyperspectral case is presented.
This work discusses an improvement to the boundary tracking algorithm introduced by Chen et al 2011. This method samples points in an image locally and utilizes the CUSUM algorithm to reduce tracking problems due to noise or texture. However, when tracking problems do arise, the local nature of the algorithm does not give any mechanism in which to recover. This work introduces a second CUSUM algorithm to detect off-boundary movement, compensating for such movement by backtracking. Boundary tracking results comparing the two algorithms are presented, including both image data and a numerical comparison of the effectiveness of the algorithms.
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