基于线性化核标签融合的脑MR图像分割方法

深层脑结构的形态变化和神经退行性疾病相关, 对脑MR图像中的深层脑结构分割有助于分析各结构的形态变化.多图谱融合方法利用图谱图像中的先验信息, 为脑结构分割提供了一种有效的方法.大部分现有多图谱融合方法仅以灰度值作为特征, 然而深层脑结构灰度分布之间重叠的部分较多, 且边缘不明显.为克服上述问题, 本文提出一种基于线性化核多图谱融合的脑MR图像分割方法.首先, 结合纹理与灰度双重特征, 形成增强特征用于更好地表达脑结构信息.其次, 引入核方法, 通过高维映射捕获原始空间中特征的非线性结构, 增强数据间的判别性和线性相似性.最后, 利用Nyström方法, 对高维核矩阵进行估计, 通过特征值分解计算虚样本, 并在核标签融合过程中利用虚样本替代高维样本, 大大降低了核标签融合的计算复杂度.在三个公开数据集上的实验结果表明, 本文方法在较少的时间消耗内, 提高了分割精度.

Linearized Kernel-Based Label Fusion Method for Brain MR Image Segmentation

Morphological changes in subcortical brain structures are related to different neurodegenerative disorders. Therefore, subcortical brain segmentation in MRI contribute to analyses of morphological changes in various structures. Multi atlas-based method provides an effective way for subcortical brain segmentation by using prior information in atlas. Most of the existing multi atlas-based methods only use intensities as features, while the distribution of gray value overlaps more and the edges of structures are not obvious. In order to solve above problems, a brain magnetic resonance image (MRI) segmentation method based on linearized kernel-based label fusion method is proposed in this paper. First, augmented features are formed by concatenating texture features and intensity features to obtain better representation. Then, kernel method is introduced to capture the nonlinear structure of features in the original space and enhance discriminability and linear similarity between features using high dimensional mapping. Finally, the Nyström method is used to estimate high dimensional kernel matrices and virtual samples are calculated by eigenvalue decomposition. Mapped samples are replaced by virtual samples in label fusion methods, which can greatly reduce the computational complexity of kernel-based label fusion methods. Experimental results on three public datasets show that the proposed method improves the segmentation accuracy with less time consumption.