基于图割的扩散张量磁共振图像胼胝体分割算法

扩散张量磁共振成像过程易因噪声导致扩散张量图像(diffusion tensor images,DTI)的体素数据发生畸变,使分割效果不佳.针对该问题,提出了一种基于图割的DTI胼胝体分割算法,该算法在求解能量函数的过程中,用非种子点与作为硬约束条件的种子点之间的J-散度中位数表示T-连接权值,用取值范围在(0,1]之间的单调递减指数函数表示N-连接权值,同时构造网格图结构,用最大流/最小切算法计算最小切,实现图像的全局最优二值化分割.DTI图像的分割实验结果表明:所提算法能更为准确地从受噪声影响的数据中提取出胼胝体,各参数不同取值时的重叠率指标统计分析也证明了新算法具有较高的分割精度.

Graph Cut Based Algorithm for Corpus Callosum Segmentation From Diffusion Tensor Images

Noises that usually appear in the process of diffusion tensor magnetic resonance imaging could result in voxel data distortion in diffusion tensor images and poor segmentation. A method based on graph cut is proposed to deal with this problem. In the process of solving the energy function of this method,Tlink weights are computed using the median of the J-divergence values,one of tensor dissimilarity metrics,among non-seed points and the seed points that are regarded as hard constraints. Meanwhile,Nlink weights are computed using a monotonically decreasing exponential function ranged in( 0,1]. At the same time,we construct a grid graph,and calculate the minimum cut through max-flow / min-cut algorithm to achieve the global optimal binary segmentation of images. Experimental results showed that the proposed method extracted corpus callosum more accurately from the white matter tissue,and statistical analysis of overlap rates with different values of parameters also proved the new algorithm has higher accuracy of the segmentation.