TY - JOUR AU - Pravitasari, Anindya Apriliyanti AU - Iriawan, Nur AU - Nurul Solichah, Siti Azizah AU - Irhamah, Irhamah AU - Fithriasari, Kartika AU - Purnami, Santi Wulan AU - Ferriastuti, Widiana PY - 2020/12/01 Y2 - 2024/03/28 TI - Gaussian Mixture Model for MRI Image Segmentation to Build a Three-Dimensional Image on Brain Tumor Area JF - MATEMATIKA JA - MATEMATIKA VL - 36 IS - 3 SE - Articles DO - 10.11113/matematika.v36.n3.1222 UR - https://matematika.utm.my/index.php/matematika/article/view/1222 SP - 217-234 AB - <span class="fontstyle0">A brain tumor is one of the deadly diseases that attack the central and nervous<br />system. The treatment of brain tumor, need high accuracy and precision. Brain tumor<br />detection through Magnetic Resonance Imaging (MRI) has two-dimensional output with<br />three perspectives, namely sagittal, coronal, and axial. These different perspectives need<br />to be seen one by one to determine the location and size of the tumor. To<br />solve the problem, this study constructs the three-dimensional visualization perspective of<br />MRI images. The tumor area in MRI image is segmented as a region of interest (ROI) by<br />employing the Gaussian Mixture Model (GMM) with Expectation-Maximization as the<br />optimization technique. These couple segmentation methods have revealed significant gain<br />as a clear boundary of the tumor area to separate from the healthy part of the brain and<br />an estimated tumor volume from sagittal, coronal, and axial perspectives. Furthermore,<br />these findings have been successfully visualized in 3D construction of the tumor position<br />on the left side of the patient’s head with an estimated volume of </span>749mm<sup>3</sup>. ER -