Gaussian Mixture Model for MRI Image Segmentation to Build a Three-Dimensional Image on Brain Tumor Area

Abstract

A brain tumor is one of the deadly diseases that attack the central and nervoussystem. The treatment of brain tumor, need high accuracy and precision. Brain tumordetection through Magnetic Resonance Imaging (MRI) has two-dimensional output withthree perspectives, namely sagittal, coronal, and axial. These different perspectives needto be seen one by one to determine the location and size of the tumor. Tosolve the problem, this study constructs the three-dimensional visualization perspective ofMRI images. The tumor area in MRI image is segmented as a region of interest (ROI) byemploying the Gaussian Mixture Model (GMM) with Expectation-Maximization as theoptimization technique. These couple segmentation methods have revealed significant gainas a clear boundary of the tumor area to separate from the healthy part of the brain andan estimated tumor volume from sagittal, coronal, and axial perspectives. Furthermore,these findings have been successfully visualized in 3D construction of the tumor positionon the left side of the patient’s head with an estimated volume of 749mm3.