MRI-Based Brain Tumor Segmentation using Modified Stable Student’s t from Burr Mixture Model with Bayesian Approach

A. P. Pravitasari*, N. Iriawan, M. A.I. Safa, Irhamah, K. Fithriasari, S. W. Purnami, W. Ferriastuti

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Finite Mixture Models have been developed for brain tumor image segmentation using the Magnetic Resonance Imaging (MRI) as a media. The goal is to obtain the best fit model with the appropriate segmentation results to describe the Region of interest (ROI). Image segmentation techniques with mixture model are used for clustering pixels based on the same color intensity (grayscale).Many studies of mixture models using asymmetric distributions, such as skew normal and skew-t distribution, have been expanded, owing to the fact that the data pattern in the MRI is not always symmetrical. Furthermore, the research uses some approaches to adaptively capture data pattern and capable of accommodating the skew and even the thicker tails than normal distribution. This technique is called the Neo-Normal. MRI-based segmentation using Modified Stable Student’s t from Burr (MSTBurr) distribution was proposed with the aim of creating an adaptive segmentation method which would be adapt to MRI data pattern distribution changes. The segmentation model is optimized by employing the Bayesian method coupled along with the Markov chain Monte Carlo (MCMC) approach because the analytical solutions are considered complicated. The results of the analysis demonstrated indicate that the MSTBurr Mixture Model (MSTBurr-MM) could capture the pattern of MRI brain tumor image better than the Gaussian Mixture Model (GMM) approach.

Original languageEnglish
Pages (from-to)297-310
Number of pages14
JournalMalaysian Journal of Mathematical Sciences
Volume13
Issue number3
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Bayesian
  • Image Segmentation
  • MSTBurr
  • Mixture Model
  • Neo-Normal

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