In recent times, ROI-based extraction and volume estimation of various brain tissue types has gained immense attention from medical and computational research community. In diagnosing certain diseases, the volume of a specific brain region (e.g. Hippocampus) needs to be estimated to the best possible accuracy. Compared to the whole brain approaches, Region of Interest (ROI)-based approaches require an additional task of locating the ROI on subjects MRI. Given that no two brains are of the same size and shape, automatic extraction of ROI requires a certain degree of sophistication. The goal is to extract a particular region from brain imaging data and estimate the volumes of different tissue types (i.e., grey matter, white matter, and cerebro-spinal fluid) automatically. The experiments are conducted with ROI extraction using two softwares, namely, SPM and itk-SnAP. In the first one, whole brain segmentation is performed on normalized images and then a predefined mask is used to extract region of interest. itk-SnAP uses a region growing approach for segmentation. Results of these methods are compared and the analysis shows that both methods give comparable results. However, it is observed that itk-SnAP is more robust for calculating volumes of small regions. In this paper we proposed a pipelining process which contains different stages like normalization, ROI masks generation, segmentation and volume estimation by using VBM5 and itk-SnAP tools. © 2011 IEEE.