A volumetric object detection framework with dual-energy CT

In this paper we present a new efficient framework that implements volumetric object detection for dual energy CT. First, we improve the original H-L curve table method with 28 elements, to get accurate material information by reconstruction. Then, we use the material information to localize suspicious regions and apply our volumetric object detection function. We reduce the dimension of the detection from 3D to 2D by using central slices, and use boosting 2d Haar features to achieve the 2D detection. Finally, we succeed in locating the target in our real world circumstance simulated baggage.