  Magnetic Resonance Imaging (MRI) is an important diagnostic tool for precise
detection of various pathologies. Magnetic Resonance (MR) is more preferred
than Computed Tomography (CT) due to the high resolution in MR images which
help in better detection of neurological conditions. Graphical user interface
(GUI) aided disease detection has become increasingly useful due to the
increasing workload of doctors. In this proposed work, a novel two steps GUI
technique for brain tumor segmentation as well as Brodmann area detec-tion of
the segmented tumor is proposed. A data set of T2 weighted images of 15
patients is used for validating the proposed method. The patient data
incor-porates variations in ethnicities, gender (male and female) and age
(25-50), thus enhancing the authenticity of the proposed method. The tumors
were segmented using Fuzzy C Means Clustering and Brodmann area detection was
done using a known template, mapping each area to the segmented tumor image.
The proposed method was found to be fairly accurate and robust in detecting
tumor.
