GVF snake algorithm-a parallel approach

Abstract

Multicore architecture is an emerging computer technology where multiple processing element will be acting as independent processing cores by sharing a common memory. Digital image segmentation is a widely used medical imaging application to extract regions of interest. GVF Active Contour is a region based segmentation technique which extracts curved and irregular shaped regions by diffusing gradient vectors and by the influence of internal and external forces. This requires prior knowledge on the geometric position and anatomical structures to locate the specific region defined within an image domain. This process requires complex mathematical calculations which in turn results in the immense consumption of CPU processing time. This may adversely affect the overall performance efficiency of the process. With the advancements in multicore technology, this processing time delay can be reduced by adapting parallelization in the computation of GVF field to the specific region of interest which is to be segmented. OpenMP is a shared memory parallel programming construct, which could implement multicore parallelism with extensive and powerful APIs thereby supporting the functionalities required to attain parallelism. This article provides a high level overview of OpenMP, its effectiveness and ease of implementation in adapting parallelism to existing traditional sequential methods using instruction, data and loop level parallelism. Performance comparison could be done with sequential versions of the program written in Matlab, Java and C languages with the proposed parallelized version of OpenMP. The result is also comparable with different operating systems like Windows and Linux.