A Metric Method Using Multidimensional Features for Nonrigid Registration of Medical Images
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摘要: 医学图像的非刚性配准对于临床的精确诊疗具有重要意义.待配准图像对中目标的大形变和灰度分布呈各向异性给非刚性配准带来困难.本文针对这个问题，提出基于多维特征的联合Renyi α-entropy度量结合全局和局部特征的非刚性配准算法.首先，采用最小距离树构造联合Renyi α-entropy，建立多维特征度量新方法.然后，演绎出新度量准则相对于形变模型参数的梯度解析表达式，采用随机梯度下降法进行参数寻优.最终，将图像的Canny特征和梯度方向特征融入新度量中，实现全局和局部特征相结合的非刚性配准.通过在36对宫颈磁共振（Magnetic resonance，MR）图像上的实验，该方法的配准精度相比较于传统互信息法和互相关系数法有明显提高.这也表明，这种度量新方法能克服因图像局部灰度分布不一致造成的影响，一定程度地减少误匹配，为临床的精确诊疗提供科学依据.
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关键词:
- 非刚性配准 /
- 联合Renyi α-entropy /
- 最小距离树 /
- 局部特征 /
- 自由形变模型
Abstract: Nonrigid registration of medical images has great significance for accurate diagnosis and therapy in clinic. It is difficult to register the images containing large deformation of object region and data anisotropy. According to this problem, an algorithm of nonrigid registration based on joint Renyi α-entropy is proposed in this paper, which combines global features with local features. Firstly, minimum spanning tree is employed for construction of joint Renyi α-entropy. A new metric is built on multidimensional features. And then, the analytical derivative of the new metric with respect to the parameters of deformation model is derived, in order to find the optima by a stochastic gradient descent method. Finally, Canny feature and gradient orientation feature of images are merged into the new metric, which implements nonrigid registration including global and local features. Experiments are performed on 36 cervical magnetic resonance (MR) image pairs. Compared to the traditional mutual information and correlation coefficient, the registration accuracy is improved significantly. It also manifests that the proposed method is able to overcome the adverse effects of local intensity inhomogeneity, and provides scientific evidence for accurate diagnosis and therapy in clinic, due to reducing mismatch in some degree. -
图 1 膀胱和宫颈放射治疗前后磁共振(Magnetic resonance, MR)图像对比
Fig. 1 Contrast of magnetic resonance (MR) images before and after radiotherapy at the bladder and the cervix
图 4 仿射粗配、互相关系数、互信息、基于图的广义互信息和本文方法的重叠率对比boxplot图(对于每一个解剖结构, 最左边的列显示了仿射粗配的结果, 第2列显示了互相关系数的结果, 第3列显示了互信息的结果, 第4列显示了基于图的广义互信息的结果, 最右边的列显示了本文方法的结果.一个星号表示两种方法重叠率中值在统计上的明显差异.)
Fig. 4 The boxplot of overlap scores using affine matching, CC, MI, GMI and our proposal (For each anatomical structure, the leftmost column shows the result for affine matching. The second column shows the result for CC. The third column shows the result for MI. The fourth column shows the result for GMI. The rightmost column shows the result for our proposal. A star indicates a statistical significant difference of the median overlaps of the two methods.)
图 5 配准结果示例, 参考图像与被形变的浮动图像采用Checkerboard模式融合
Fig. 5 Demonstration of the registration result, and the reference image is combined with the deformed moving image, using a Checkerboard pattern
表 1 所有配准DSC的均值和方差
Table 1 The mean and variance of DSC about all registration results
结构区域 配准方法 DSC值 仿射粗配 0.7618±0.0123 CC 0.8271±0.0056 CTV MI 0.8332±0.0756 GMI 0.8460±0.0706 本文方法 0.8462±0.0684 仿射粗配 0.6756±0.0198 CC 0.7386±0.0200 Bladder MI 0.7375±0.1362 GMI 0.7697±0.1332 本文方法 0.7800±0.1313 仿射粗配 0.6861±0.0083 CC 0.7338±0.0042 Rectum MI 0.7410±0.0673 GMI 0.7566±0.0627 本文方法 0.7680±0.0577 
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