Automated Tumor Segmentation and Brain Mapping for the Tumor Area
This work addresses brain tumor diagnosis for doctors by providing an automated GUI tool, but it is incremental as it combines existing methods like Fuzzy C Means Clustering with known templates.
The authors tackled brain tumor segmentation and Brodmann area mapping from MRI images using a two-step GUI technique, achieving fairly accurate and robust detection on a dataset of 15 patients.
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.