Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:644-647. doi: 10.1109/EMBC.2017.8036907.
Finite mixture model (FMM) has been widely used for unsupervised segmentation of magnetic resonance (MR) images in recent years. However, in real applications, the distribution of the observed data usually contains an unknown fraction of outliers, which would interfere with the estimation of the parameters of the mixture model. The statistical model-based technique which provides a theoretically well segmentation criterion in presence of outliers is the mixture modeling and the trimming approach. Therefore, in this paper, a robust estimation of asymmetric Student's-t mixture model (ASMM) using the trimmed likelihood estimator for MR image segmentation has been proposed. The proposed method is supposed to discard the outliers, and then to estimate the parameters of the ASMM with the remaining samples. The advantages of the proposed algorithm are that its robustness to dispose the disturbance of outliers and its flexibility to describe various shapes of data. Finally, expectation-maximization (EM) algorithm is adopted to maximize the log-likelihood and to obtain the estimation of the parameters. The experimental results show that the proposed method has a better performance on the segmentation of synthetic data and real MR images.
近年来，有限混合模型（FMM）已被广泛用于磁共振（MR）图像的无监督分割。然而，在实际应用中，观测数据的分布通常包含未知比例的异常值，这会干扰混合模型参数的估计。基于统计模型的技术，即在存在异常值的情况下提供理论上良好的分割标准的技术是混合建模和修剪方法。因此，本文提出了一种使用修剪似然估计器对非对称学生t混合模型（ASMM）进行鲁棒估计以用于MR图像分割的方法。所提出的方法旨在舍弃异常值，然后用剩余样本估计ASMM的参数。该算法的优点在于其处理异常值干扰的鲁棒性以及描述各种数据形状的灵活性。最后，采用期望最大化（EM）算法来最大化对数似然并获得参数估计。实验结果表明，所提出的方法在合成数据和真实MR图像的分割上具有更好的性能。
