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 state-of-the-art 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 cross-section 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
cross-section is approximately a circle. A robust Level Set method
is then applied to compensate the approximation error and
efficiently estimate the cross-section. 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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