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

Pravitasari, Anindya Apriliyanti; Iriawan, Nur; Solichah, Siti Azizah Nurul; Irhamah; Fithriasari, Kartika; Purnami, Santi Wulan; Ferriastuti, Widiana

A brain tumor is one of the deadly diseases that attack the central and nervous system. The treatment of brain tumor, need high accuracy and precision. Brain tumor detection through Magnetic Resonance Imaging (MRI) has two-dimensional output with three perspectives, namely sagittal, coronal, and axial. These different perspectives need to be seen one by one to determine the location and size of the tumor. To solve the problem, this study constructs the three-dimensional visualization perspective of MRI images. The tumor area in MRI image is segmented as a region of interest (ROI) by employing the Gaussian Mixture Model (GMM) with Expectation-Maximization as the optimization technique. These couple segmentationmethods have revealed significant gain as a clear boundary of the tumor area to separate from the healthy part of the brain and an estimated tumor volume from sagittal, coronal, and axial perspectives. Furthermore, these findings have been successfully visualized in 3D construction of the tumor position on the left side of the patient's head with an estimated volume of 749 mm³.

GAUSSIAN mixture models; THREE-dimensional imaging; MAGNETIC resonance imaging; BRAIN tumors; BRAIN imaging

Matematika, 2020, Vol 36, p217

0127-8274

Academic Journal

10.11113/matematika.v36.n3.1222