Open AccessUnsupervised Learning for MRI Brain Tumor Segmentation with Spatially Variant Finite Mixture Model in Reversible Jump MCMC Algorithm
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 - 10.1088/1742-6596/1776/1/012041
Subject(s) - segmentation , mixture model , reversible jump markov chain monte carlo , artificial intelligence , computer science , pattern recognition (psychology) , bayesian probability , image segmentation , algorithm , matching (statistics) , jump , process (computing) , bayesian inference , mathematics , statistics , operating system , physics , quantum mechanics
MRI brain tumor segmentation is an important topic in medical image processing. Manual segmentation is risky and time-consuming when the MRI is in low quality. The automatic segmentation can be a solution to manage this problem. This paper proposed an improved modeling approach for unsupervised learning trough Spatially Variant Finite Mixture Model (SVFMM). The main contribution is the automation of the SVFMM algorithm to find the optimum number of clusters. This is achieved by employing the birth-death random process in Bayesian Reversible Jump MCMC. Validation of the proposed model is done by calculating the Correct Classification Ration (CCR) in comparison to the original SVFMM and Gaussian Mixture Model (GMM). The proposed model provides similar performance in image segmentation compared to the original SVFMM but is better than GMM. However, SVFMM-RJMCMC is faster and more efficient in finding the optimum number of clusters.