Segmentation of the spinal cord region is an imperative step in the automated analysis of neurological ailments such as multiple sclerosis. Multiple studies demonstrated the connection between progression of neurological diseases and measurements identifying with spinal cord atrophy and changes to its structure. Segmentation of spinal cord region manually or semi-automatically, can be conflicting and tedious for large datasets. We present a novel automated method, that segments the spinal cord region, utilizing circular active discs and region growth algorithm. The proposed method is validated on the Visible Human Project dataset. The results with regards to sensitivity, specificity, accuracy, Jaccard index, and Dice coefficient were 97.23%, 100%, 99.76%, 96.83%, and 98.65%, respectively. The results were observed to be highly precise in comparison to expert outlines.