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首页> 外文期刊>Image Processing, IET >Brain MRI segmentation by combining different MRI modalities using Dempster–Shafer theory
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Brain MRI segmentation by combining different MRI modalities using Dempster–Shafer theory

机译：使用Dempster-Shafer理论结合不同的MRI方式进行脑MRI分割

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摘要

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.
机译：磁共振成像（MRI）具有不同的模态，包括T1和T2加权，PD（质子密度）和Flair图像。当应对诸如强度不均匀，部分体积效应和噪音等伪影时，脑部MRI分割是一个挑战。由于伪影改变了MRI模态不同部分的强度，因此高度不确定这些模态的强度。在这里，建议将Dempster-Shafer理论和模糊聚类结合起来用于脑部MRI分割，因为它们具有鲁棒性。这项研究的目的是提供一种基于不同形式的数据融合来分割脑部MRI的技术。 T1，T2，PD和Flair用于这项研究。模糊聚类和特定映射用于形成Dempster-Shafer信念结构。为了评估该算法的效率，进行了几次仿真，并使用Dice和Jaccard系数将结果与其他方法进行了比较。该算法的定性和定量结果验证了该算法的成功。该方法比显示最佳结果的先前方法提高了3-4％。

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