The image foresting transform(IFT) is a powerful graph-based framework for the design and implementation of image processing operators. In this work we present the Partitioned IFT (PIFT), an algorithm that computes any IFT operator as a series of independent IFT-like computations. The PIFT makes parallelization of existing IFT operators easy, and allows the computation of IFTs in systems with scarce memory. We evaluate the PIFT for two image processing applications: watershed segmentation and Euclidean distance transforms
