A multiscale, decision-theoretic algorithm for anomaly detection in images based upon scattered radiation

Presents a multiscale, statistically-based algorithm for the detection and characterization of localized anomalies in a medium based upon observations of scattered energy obtained along the boundaries of the region. The authors use wavelet transform methods for lowering the computational complexity of the detection and estimation procedures and for providing efficient and physically meaningful models for the non-anomalous behavior of the medium. Determination of the structures of the anomalous areas is performed in a scale-recursive manner using a sequence of generalized, M-ary likelihood ratio tests to successively subdivide the region under consideration as a means of localizing anomalies both in space and scale. Examples of the approach are demonstrated using the Born approximation to a 2D inverse conductivity problem arising in geophysical prospecting applications