The aim of this paper is to study a topological derivative based anomaly detection algorithm. We compare its performance with other imaging approaches such as MUltiple SIgnal Classification, backpropagation, and Kirchhoff migration. We also investigate its stability with respect to medium and measurement noises as well as its resolution. A simple postprocessing of the data set is introduced and shown to be essential in order to obtain an efficient topological based imaging functional, both in terms of resolution and stability.