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Magnetic resonance imaging (MRI) is used to visualize the anatomy and structure of a body organ for assistance in medical diagnostics of certain disease or conditions and to evaluate a particular disease. Magnetic resonance images of a specified anatomy are constructed by using radio waves, a magnetic field and compute. This technical paper demonstrates the segmentation process of brain MR images by using Region Growing and Seed Pixel methods. Segmentation is a noteworthy phase in the various image processing applications. Automatic Brain MRI Segmentation is a simple, robust and efficient image segmentation algorithm for classifying brain tissues form dual echo Magnetic Resonance (MR) Images. The designed system incorporates this robust ability of the described algorithm to segment the various parts of brain MR image automatically. The utilized algorithm consists of an assortment of components as adaptive histogram analysis, threshold, and region growing segmentation. These vigorous techniques are used for the sake of accurate categorization of assorted brain regions such as the brain white, gray matter, cerebrospinal fluid and ventricular regions. The orthodox techniques exploited for the analysis of a sequence of MR images was time consuming and inefficient. The conducted research minimizes this overhead by using the semi-automated designed system which has been tested successfully on multiple Dicom standard MRI real brain images.