Li Chunming, Xu Chenyang, Anderson Adam W, Gore John C
Vanderbilt University Institute of Imaging Science, Nashville, TN 37232, USA.
Inf Process Med Imaging. 2009;21:288-99. doi: 10.1007/978-3-642-02498-6_24.
This paper presents a new energy minimization method for simultaneous tissue classification and bias field estimation of magnetic resonance (MR) images. We first derive an important characteristic of local image intensities--the intensities of different tissues within a neighborhood form separable clusters, and the center of each cluster can be well approximated by the product of the bias within the neighborhood and a tissue-dependent constant. We then introduce a coherent local intensity clustering (CLIC) criterion function as a metric to evaluate tissue classification and bias field estimation. An integration of this metric defines an energy on a bias field, membership functions of the tissues, and the parameters that approximate the true signal from the corresponding tissues. Thus, tissue classification and bias field estimation are simultaneously achieved by minimizing this energy. The smoothness of the derived optimal bias field is ensured by the spatially coherent nature of the CLIC criterion function. As a result, no extra effort is needed to smooth the bias field in our method. Moreover, the proposed algorithm is robust to the choice of initial conditions, thereby allowing fully automatic applications. Our algorithm has been applied to high field and ultra high field MR images with promising results.
本文提出了一种用于磁共振（MR）图像的组织分类和偏置场估计同时进行的新能量最小化方法。我们首先推导局部图像强度的一个重要特征——邻域内不同组织的强度形成可分离的簇，并且每个簇的中心可以通过邻域内的偏置与一个组织相关常数的乘积很好地近似。然后，我们引入一个相干局部强度聚类（CLIC）准则函数作为评估组织分类和偏置场估计的度量。该度量的积分在偏置场、组织的隶属函数以及从相应组织近似真实信号的参数上定义了一个能量。因此，通过最小化这个能量可以同时实现组织分类和偏置场估计。CLIC准则函数的空间相干性质确保了所推导的最优偏置场的平滑性。结果，在我们的方法中不需要额外努力来平滑偏置场。此外，所提出的算法对初始条件的选择具有鲁棒性，从而允许全自动应用。我们的算法已应用于高场和超高场MR图像，取得了有希望的结果。
