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Segmentation of the subcortical structures in the brain such as the hippocampus, is known to be very challenging owing to its’ image characteristics. In brain MR images, the hippocampus is observed as a gray matter structure that often exhibits very weak or unclear boundary definitions at some fragments of its’ boundary. The unclear boundaries even cause the medical experts to misjudge the hippocampus boundary, especially at the head and tail. In this research, an automated segmentation approach, termed as Assembled and Weighted Coherent Point Drift is investigated to delineate the hippocampus accurately. Evaluations on public datasets produced an average Dice Similarity Coefficient of 0.8050, which appears better, in comparison to several other hippocampus segmentation approaches, especially against the well-known software program called Freesurfer. The study also revealed that the accuracy of the proposed segmentation approach seems on par with other various state-of-the-art approaches.