Parallel computing in digital image processing

Abstract

Application with sequential algorithm can no longer rely on technology scaling to improve performance. Image processing applications exhibits high degree of parallelism and are excellent source for multi-core platform. Major challenge of parallel processing is not only aim to high performance but is to give solution in less time and better utilization of resources. Medical imaging require more computing power than a traditional sequential computer can do and we also know that for medical imaging, it is necessary that the image is clear and be obtained as quickly as possible. We can achieve through the process of parallelizing. Parallelizing optimizes the speed at which the image is produced.This paper presents the different types of parallelism in image processing i.e., data, task and pipeline parallelism. This paper also discusses three types of operators; point operators, neighborhood operators and global operators used for image processing. Different algorithms used for parallel image processing are discussed and the application of medical imaging is discussed using work flow engine Taverna for scientific processing.