Segmentation subdivides an image into its regions of components or objects and an important
tool in medical image processing [1]. As an initial step segmentation can be used for
visualization and compression. Through identifying all pixels (for two dimensional image) or
voxels (for three dimensional image) belonging to an object, segmentation of that particular
object is achieved [2]. In medical imaging, segmentation is vital for feature extraction, image
measurements and image display [2, 3]. Segmentation of the brain structure from magnetic
resonance imaging (MRI) has received paramount importance as MRI distinguishes itself from
other modalities and MRI can be applied in the volumetric analysis of brain tissues such as
multiple sclerosis, schizophrenia, epilepsy, Parkinson’s disease, Alzheimer’s disease, cerebral
atrophy, etc [4]. Graph cuts is one the image segmentation techniques which is initiated by
interactive or automated identification of one or more points representing the 'object'. In this
technique one or more points representing the 'background' are called seeds and serve as
segmentation hard constraints where as the soft constraints reflect boundary and/or region
information. An important feature of this technique is its ability to interactively improve a
previously obtained segmentation in an efficient way.