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首页> 外文期刊>Biomedical Engineering: Applications, Basis and Communications >INTERLEAVED EM SEGMENTATION FOR MR IMAGE WITH INTENSITY INHOMOGENEITY
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INTERLEAVED EM SEGMENTATION FOR MR IMAGE WITH INTENSITY INHOMOGENEITY

机译：强度不均一的MR图像的交错EM段

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摘要

Expectation-maximization (EM) algorithm has been extensively applied in brain MR image segmentation. However, the conventional EM method usually leads to severe misclassifications MR images with bias field, due to the significant intensity inhomogeneity. It limits the applications of the conventional EM method in MR image segmentation. In this paper, we proposed an interleaved EM method to perform tissue segmentation and bias field estimation. In the proposed method, the tissue segmentation is performed by the modified EM classification, and the bias field estimation is accomplished by an energy minimization. Moreover, the tissue segmentation and bias field estimation are performed in an interleaved process, and the two processes potentially benefit from each other during the iteration. A salient advantage of the proposed method is that it overcomes the misclassifications from the conventional EM classification for the MR images with bias field. Furthermore, the modified EM algorithm performs the soft segmentation in our method, which is more suitable for MR images than the hard segmentation achieved in Li et al.'s~(12) method. We have tested our method in the synthetic images with different levels of bias field and different noise, and compared with two baseline methods. Experimental results have demonstrated the effectiveness and advantages of the proposed algorithm.
机译：期望最大化（EM）算法已广泛应用于脑MR图像分割。然而，由于明显的强度不均匀性，常规的EM方法通常导致带有偏置场的严重的MR图像误分类。它限制了常规EM方法在MR图像分割中的应用。在本文中，我们提出了一种交错的EM方法来执行组织分割和偏场估计。在提出的方法中，通过改进的EM分类执行组织分割，并且通过最小化能量来完成偏置场估计。此外，组织分割和偏置场估计是在交错过程中执行的，并且这两个过程在迭代过程中可能彼此受益。所提出的方法的显着优点是，它克服了常规EM分类对具有偏置场的MR图像的错误分类。此外，改进的EM算法在我们的方法中执行了软分割，比Li et al。（12）方法中实现的硬分割更适合MR图像。我们已经在具有不同水平的偏置场和不同噪声的合成图像中测试了我们的方法，并与两种基线方法进行了比较。实验结果证明了该算法的有效性和优势。

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