This paper takes a step towards the extraction of contrastive explanations between anoma-lies and the intrinsic structure of regular points. It proposes a variant of the isolation forest algorithm whose main objective is to preserve the structure of regular data in order to facilitate its reconstruction. Experiments conducted on synthetic datasets show that this variant of isolation forest deteriorates less the structure of regular data than the classical method. Therefore, the former can serve as a basis for a unified approach to anomaly detection and explanation.