Conventional cone-beam computed tomography CT (CBCT) provides limited discrimination between low-contrast tissues. Furthermore, it is limited to full-spectrum energy integration. A dual-energy CBCT system could be used to separate photon energy spectra with the potential to increase the visibility of clinically relevant features and acquire additional information relevant in a multitude of clinical imaging applications. In this work, the performance of a novel dual-layer dual-energy CBCT (DL-DE-CBCT) C-arm system is characterized for the first time. Methods: A prototype dual-layer detector was fitted into a commercial interventional C-arm CBCT system to enable DL-DE-CBCT acquisitions. DL-DE reconstructions were derived from material-decomposed Compton scatter and photoelectric base functions. The modulation transfer function (MTF) of the prototype DL-DE-CBCT was compared to that of a commercial CBCT. Noise and uniformity characteristics were evaluated using a cylindrical water phantom. Effective atomic numbers and electron densities were estimated in clinically relevant tissue substitutes. Iodine quantification was performed (for 0.5-15 mg/ml concentrations) and virtual noncontrast (VNC) images were evaluated. Finally, contrast-to-noise ratios (CNR) and CT number accuracies were estimated. Results: The prototype and commercial CBCT showed similar spatial resolution, with a mean 10% MTF of 5.98 cycles/cm and 6.28 cycles/cm, respectively, using a commercial standard reconstruction. The lowest noise was seen in the 80 keV virtual monoenergetic images (VMI) (7.40 HU) and the most uniform images were seen at VMI 60 keV (4.74 HU) or VMI 80 keV (1.98 HU), depending on the uniformity measure used. For all the tissue substitutes measured, the mean accuracy in effective atomic number was 98.2% (SD 1.2%) and the mean accuracy in electron density was 100.3% (SD 0.9%). Iodine quantification images showed a mean difference of −0.1 (SD 0.5) mg/ml compared to the true iodine concentration for all blood and iodine-containing objects. For VNC images, all blood substitutes containing iodine averaged a CT number of 43.2 HU, whereas a blood-only substitute measured 44.8 HU. All water-containing iodine substitutes measured a mean CT number of 2.6 in the VNC images. A noise-suppressed dataset showed a CNR peak at VMI 40 keV and low at VMI 120 keV. In the same dataset without noise suppression applied, a peak in This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

In patients with NSTE-ACS and elevated cardiac troponin T levels, an early invasive strategy has no benefit over a selective invasive strategy in reducing the 10-year composite outcome of death or spontaneous MI, and a selective invasive strategy may be a viable option in selected patients.

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.

Changing image intensities causes problems for many computer vision applications operating in unconstrained environments. We propose generally applicable algorithms to correct for global differences in intensity between images recorded with a static or slowly moving camera, regardless of the cause of intensity variation. The proposed intensity correction is based on intensityquotient estimation. Various intensity estimation methods are compared. Usability is evaluated with background classification as example application. For this application we introduced the PIPE error measure evaluating performance and robustness to parameter setting. Our approach retains local intensity information, is always operational and can cope with fast changes in intensity. We show that for intensity estimation, robustness to outliers is essential for dynamic scenes. For image sequences with changing intensity, the best performing algorithm (MofQ) improves foregroundbackground classification results up to a factor two to four on real data. P.J. Withagen was during this research associated with