The passengers flow at the US airports increased in the recent years. The larger number of passengers demands for lower number of false alarms and higher accuracy of threat detection at the time of baggage screening. This paper presents an algorithm to detect and extract possible explosive containers in X-Ray- CT bags images. The algorithm is composed by three main stages. The 1st one makes the threat container excels among the other objects in the bag image. The 2nd approach: Extracts the SURF features from the query and the bag images; Matches the SURF feature vectors from the two images. The bag image points (pixels), at which the best match is found, define regions of interest (RoI). Different RoI in a bag are identified by separate clusters of points. At the 3rd stage of the algorithm an enlarging active contour (AC) extracts the boundary of every RoI. The starting point of every AC is the mass center of the corresponding cluster of SURF points. The theory is validated on a number of X-ray/CT images. A qualitative comparison with contemporary methods outlines the advantages and the contribution of the present algorithm.