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40 AbstractPurpose: To evaluate in an in vitro model the feasibility and accuracy of Roentgen stereophotogrammetric analysis (RSA) versus computed tomography (CT) for the ability to detect stent-graft migration.Methods: An aortic model was constructed from a 22-mm-diameter Plexiglas tube with 6-mm polytetrafluoroethylene inlays to mimic the renal arteries. Six tantalum markers were placed in the wall of the aortic tube proximal to the renal arteries. Another 6 markers were added to a Gianturco stent, which was cast in Plexiglas and placed inside the aorta and fixed to a micromanipulator to precisely control displacement of the stent along the longitudinal axis.Sixteen migrations were analyzed with RSA software and compared to the micromanipulator.Thirty-two migrations were measured by 3 observers from CT images acquired with 16×0.5-mm beam collimation and reconstructed with a 0.5-mm slice thickness and a 0.4-mm reconstruction interval. Measurements were made with Vitrea postprocessing software using a standard clinical protocol and central lumen line reconstruction. Results of CT were also compared to the micromanipulator. Results:The mean RSA measurement error compared to the micromanipulator was 0.002±0.044 mm, and the maximum error was 0.10 mm. There was no statistically significant interobserver variability for CT (p=0.17). The pooled mean (maximum) measurement error of CT was 0.14±0.29 (1.00) mm, which was significantly different from the RSA measurement error (p<0.0001). Conclusion:Detection of endograft migration by RSA is feasible and was significantly more accurate than CT in this nonpulsatile in vitro model.

The aim of the research presented in this paper is to find out whether automatic classif,ication of ships from Forward Looking InfraRed (FLIR) images is feasible in maritime patrol aircraft. An image processing system has been developed for this øsk. Classification has been performed using a k-NN, a linear, and a quadratic classifier. In paficular, using the l-NN classifier, good results were achieved using a two-step classification algorithm.Keywords: FLIR, target, classiftcation, automatic target recognition, infrared 1. OVERVIEW OF LITERATURE Several methods have been described in literature to perform the automatic classification of ships from FLIR images. Note that most papers a¡e l0 to 20 years old, which is a long time for an image processing and pattern recognition application.Depending on the data used, which differs from real FLIR images [4, 8] The calculated features can be divided into th¡ee groups. Features can be calculated using the gray value distribution, by using moments and moment invariant functions U 1, l4l. Also, features can be calculated using the shape of the silhouette, like location and size of the superstructures [4, 6, 8, 9]. Beside this, the location of the hot spot, being the funnel for most ships, can be used as a feature [3].The classification of ships has been done by two classes of methods, namely k-Nea¡est Neighbor (k-l'{N) classification I l, l4l and using a binary decision tree [6, 8].The performances of all methods a¡e hard to compare because of the large variety in data and number of classes used. Results of 7U93Vo [4] and 63-907o [8] correct classihcations were obtained using real data and over eight classes.