一种面向医学图像非刚性配准的多维特征度量方法OA北大核心CSCDCSTPCD
A Metric Method Using Multidimensional Features for Nonrigid Registration of Medical Images
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Renyiα-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…查看全部>>
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 ob ject 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 Reny…查看全部>>
陆雪松;涂圣贤;张素
中南民族大学生物医学工程学院 武汉 430074上海交通大学生物医学工程学院 上海 200240上海交通大学生物医学工程学院 上海 200240
非刚性配准联合Renyiα-entropy最小距离树局部特征自由形变模型
Nonrigid registrationjoint Renyi α-entropyminimum spanning treelocal featurefree-form deformation model
《自动化学报》 2016 (9)
基于多维广义互信息的医学图像非刚性配准算法研究
1413-1420,8
国家自然科学基金(61002046),国家民委科研项目(14ZNZ024)资助Supported by National Natural Science Foundation of China (61002046) and Scientific Research Projects by the State Ethnic Affairs Commission of China (14ZNZ024)