In the past couple of years, sensor networks have evolved into an important infrastructure component for monitoring and tracking events and phenomena in several, often mission critical application domains. An important task in processing streams of data generated by these networks is the detection of anomalies, e.g., outliers or bursts, and in particular the computation of the location and spatial extent of such anomalies in a sensor network. Such information is then used as an important input to decision making processes. In this paper, we present a novel approach that facilitates the efficient computation of such anomaly regions from individual sensor readings. We propose an algorithm to derive regions with a spatial extent from individual (anomalous) sensor readings, with a particular focus on obstacles present in the sensor network and the influence of such obstacles on anomaly regions. We then improve this approach by describing a distributed in-network processing technique where the region detection is performed at sensor nodes and thus leads to important energy savings. We demonstrate the advantages of this strategy over a traditional, centralized processing strategy by employing a cost model for real sensors and sensor networks.