Extracting regions of interest applying a local watershed transformation

We present a new technique for extracting regions of interest (ROI) applying a local watershed transformation. The proposed strategy for computing catchment basins in a given region of interest is based on a rain-falling simulation. Unlike the standard watershed algorithms, which flood the complete (gradient magnitude of an) image, the proposed approach allows us to perform this task locally. Thus, a controlled region growth is performed, saving time and reducing the memory requirement especially when applied on volume data. A second problem arising from the standard watershed transformation is the over-segmented result and the lack of sound criteria for merging the computed basins. For overcoming this drawback, we present a basin-merging strategy introducing four criteria for merging adjacent basins. The threshold values applied in this strategy are derived from the user input and match rather the attributes of the selected object than of all objects in the image. In doing so, the user is not required to adjust abstract numbers, but to simply select a coarse region of interest. Moreover, the proposed algorithm is not limited to the 2D case. As we show in this work, it is suitable for volume data processing as well. Finally, we present the results of applying the proposed approach on several example images and volume data sets.