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In the digital world of medical transcription involving various dimensions of processes, detecting the edge of a standard medical image for clinical research/diagnosis, telemedicine and other applicative purposes requires various efficient and effective methodologies to address the needs of the processes. Among these various meta-heuristics, as the size of the problem tends to increase along with time, the processes and their elemental techniques, proven to have been providing viable solutions appeals for reserve management and lesser computation times, with the efficiency of such algorithms and algorithmic operations to be enhanced at suitable levels of abstraction.  In this paper we propose an effective topological algorithm, which inhibits the characteristic features of high performance parallel enumeration in such heterogeneous computation environments. The proposed scheduler in the defined topological algorithm takes into consideration the metrics generated by As Built Critical Path (ABCP) - A hybrid methodological process. These metrics are re-initialized and processed to address the management of resources and the realization of search space. We also propose a methodology for shared memory access by the ants to perform parallel computation and as well implement the optimization factor in detecting the edge. An in-depth analysis with respect to the Speedup factor and the Execution time metrics are analyzed for various scenarios under consideration. The differentiations are evaluated and plotted for further futuristic analysis

An Effective Parallelism Topology in Ant Colony Optimization algorithm for Medical Image Edge Detection with Critical Path Methodology (PACO-CPM)