A Data Parallel Algorithm for Solving the Region Growing Problem on the Connection Machine

Region growing is a general technique for image segmentation, where image characteristics are used to group adjacent pixels together to form regions. This paper presents a parallel algorithm for solving the region growing problem based on the split and merge approach, and uses it to test and compare various parallel architectures and programming models. The implementations were done on the Connection Machine, models CM-2 and CM-5, in the data parallel and message passing programming models. Randomization was introduced in breaking ties during merging to increase the degree of parallelism, and only one and two-dimensional arrays of data were used in the implementations.