A Novel Evolving Mutation Analysis approach of Hybrid Parallel Ant Colony Optimization algorithm for DICOM images

The information contained in a DICOM image is very crucial for telemedicine, tele-radiology and other medical diagnosis and prognostic applications. In the field of medical transcription there have been various techniques in detecting the edge of an image. In most of the computer vision systems, DICOM image is considered as a standard digital imaging process of storing, capturing and transmitting the required medical information of an individual. The meta-heuristic process used in detecting the edge of an image, topologically, while the evolving non-deterministic behavior, stochastically, is to be addressed through a natural selection process. In this paper we propose a hybrid genetic algorithm coupled with effective parallelism topology based Ant Colony Optimization (ACO) and Hamilton’s rule for evolutionary scenarios. The initial analysis of the algorithm showed an appreciated performance in handling the dynamic attributes of the DICOM images. This is successively enhanced with the parallelism topology, as multi-processor environment is considered which has shown an improved speed and efficiency.