Open AccessBrain MRI segmentation by combining different MRI modalities using Dempster–Shafer theory
Author(s) -
Tavakoli Fattane,
Ghasemi Jamal
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0473
Subject(s) - dempster–shafer theory , artificial intelligence , segmentation , computer science , fuzzy logic , modalities , pattern recognition (psychology) , jaccard index , magnetic resonance imaging , cluster analysis , fluid attenuated inversion recovery , fuzzy clustering , possibility theory , fuzzy set , medicine , radiology , social science , sociology
Magnetic resonance imagings (MRIs) have different modalities, including T1‐ and T2‐weighted, PD (proton density), and Flair images. Brain MRI segmentation is a challenge when coping with artefacts such as intensity non‐uniformity, partial volume effects, and noise. As artefacts change the intensity of different part of MRI modalities, describing the intensity of these modalities is highly uncertain. Here, it is proposed that the Dempster–Shafer theory and fuzzy clustering can be combined for brain MRI segmentation because of their robustness. The purpose of this research is to offer a technique based on data fusion of different modalities to segment brain MRIs. T1, T2, PD, and Flair were employed in this study. Fuzzy clustering and specific mapping were used to form the Dempster–Shafer belief structure. In order to evaluate the efficiency of the proposed algorithm, several simulations were performed and the Dice and Jaccard coefficients were used to compare the results with those of other methods. The qualitative and quantitative results of the proposed algorithm verify the success of the proposed algorithm. This method improved 3–4% over that of the previous methods which had showed the best results.