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We present a new method for the segmentation and the detection of human Abdominal Aorta in CT images. Our method is divided into two parts. In the first part we estimate the position and the dimension of the aortic lumen using stateoftheart object tracking techniques. The second part employs curve fitting methods in order to detect the boundaries of the aortic lumen with accuracy, based on the estimation of the first part. In particular, the proposed method uses the Kalman Filter to track the aortic crosssection in consecutive CT images. The observations needed by the Kalman procedure are extracted with the Circle Hough Transformation, based on the assumption that the morphological structure of the aortic crosssection is approximately a circle. A robust Level Set method is then applied to compensate the approximation error and efficiently estimate the crosssection. The algorithms and the mathematical tools developed during the project prove feasibility for an accurate and reliable method for the segmentation of the abdominal aorta from CT data, that in the future could be used to benefit patients with aortic aneurysms.
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