Parallelization of Image Segmentation Algorithms

With the rapid developments of higher resolution imaging systems, larger image data are produced. To process the increasing image data with conventional methods, the processing time increases tremendously. Image segmentation is one of the many image processing algorithms, and it is widely used in medical imaging (i.e. find tumor in MRI), robotic vision (i.e. vision-based navigation), and face recognition. New faster image processing techniques are needed to keep up with the ever increasing image data size. This paper investigates the parallelization of image segmentation techniques: Watershed Transform and K-Means Clustering algorithms. Lastly, K-Means Clustering is combined with Watershed Transform to address the over-segmentation issue of the Watershed algorithm.