Open AccessBayesian MSTBurr mixture model in the construction of 3D-MRI brain tumor images
Author(s) -
Anindya Apriliyanti Pravitasari,
Nur Iriawan,
Kartika Fithriasari,
Santi Wulan Purnami,
Irhamah,
Widiana Ferriastuti
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1722/1/012098
Subject(s) - segmentation , artificial intelligence , computer science , cluster analysis , visualization , volume rendering , pattern recognition (psychology) , computer vision , image segmentation , rendering (computer graphics) , bayesian probability , matlab , volume (thermodynamics) , brain tumor , 3d model , medicine , pathology , physics , quantum mechanics , operating system
Detection of a brain tumor could be done with the serial of MRI images. The location and size of the tumor should be determined by viewing the 2D images individually. This kind of analysis is inefficient and error-prone. For better visualization, this study reconstructs a 3D structure from 2D MRI images. In recognizing the brain tumors, image segmentation is performed using the clustering analysis via Bayesian MSTBurr Mixture Model. The optimum cluster is selected by calculating the Correct Classification Ratio. The segmentation results for each image slice are performed in 3D rendering with the Matlab Volume Viewer. This study succeeded in creating a 3D model with a segmentation accuracy of 93.66% and an estimation of the tumor volume of about 33,556 mm 3 .