The goal of image segmentation is to identify and differentiate one object or region from another. The problems associated with image segmentation or groupings have always been a challenge to the image processing community especially in the case of medical imaging where segmentation can play a key role to 3D model re-construction for medical diagnosis and planning. Though it can be said that there is no 'one-size-fits-all' algorithm available and that manual segmentation remains the best available technique, current on-going works have focused on hastening the computational intensive process and to achieve a high level of accuracy with as little human intervention as possible. This is especially true when fast segmentation is required for tomography images such as MRI or CT images. In this paper, we present an automated segmentation algorithm that utilizes an integrated hybrid method based on intensity, region growing and edge related techniques. The algorithm checks for connectivity and continuity to ensure that the segmented region is of one enclosed entity. Results have shown the algorithm to be fairly accurate and it is able to detect irregular shapes. The algorithm is also implementable on a Single Instruction Multiple Data (SIMD) or on multiple SIMD cards. The automated hybrid segmentation method can be robust, fairly accurate and simple to implement.