Brain MRI segmentation by combining different MRI modalities using Dempster–Shafer theory. Issue 8 (1st August 2018)

• Record Type: Journal Article
• Authors: Tavakoli, Fattane; Ghasemi, Jamal
• Is Part Of: IET image processing. Volume 12:Issue 8(2018)
• Page Start: 1322
• Page End: 1330
• Publication Date: 2018-08-01
• Subjects: sensor fusion -- brain -- medical image processing -- fuzzy set theory -- biomedical MRI -- image segmentation -- uncertainty handling; intensity nonuniformity -- artefacts -- different part -- Dempster–Shafer theory -- fuzzy clustering -- brain MRI segmentation -- different modalities -- segment brain MRIs -- T1 -- T2 -- Dempster–Shafer belief structure -- different MRI modalities -- magnetic resonance imagings -- PD -- Flair images; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0473
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.