| 基于边缘特征点互信息熵的医学图像配准方法 |
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| 引用本文： | 魏本征,甘洁,尹义龙.基于边缘特征点互信息熵的医学图像配准方法[J].数据采集与处理,2018,33(2):248-258. |
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| 作者姓名： | 魏本征 甘洁 尹义龙 |
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| 作者单位： | 1. 山东中医药大学理工学院, 济南, 250355;2. 山东中医药大学计算医学实验室, 济南, 250355;3. 山东中医药大学第二附属医院放射科, 济南, 250001;4. 山东大学计算机科学与技术学院, 济南, 250100 |
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| 基金项目： | 国家自然科学基金（U1201258，61572300）资助项目；山东省自然科学基金（ZR2015FM010）资助项目；山东高校科技计划（J15LN20）资助项目；山东省中医药科技发展计划（2015-026）资助项目。 |
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| 摘 要： | 基于互信息熵的图像配准方法已经被广泛应用于医学图像配准中，为克服互信息配准方法的不足，结合图像空间结构信息，本文提出一种基于边缘特征点互信息熵的医学图像配准方法，设计了包括互信息熵、图像空间结构和形状特征点等多信息融合的配准新测度。算法首先采用改进的形态学梯度提取医学图像边缘轮廓；然后构造了以边缘区域特征和梯度信息为基础的特征点互信息能量函数，并通过最小化能量函数来获取配准参数；最后，结合梯度下降法优化策略，实现图像配准。实验研究表明，该方法在保证了配准精度的同时，配准速度较快、鲁棒性较好、综合性能优良，具有一定的临床推广价值。
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| 关 键 词： | 图像配准 医学图像 互信息熵 测度函数 边缘特征 |
| 收稿时间： | 2016/6/29 0:00:00 |
| 修稿时间： | 2016/12/15 0:00:00 |
Medical Image Registration Based on Mutual Information Entropy Combined with Edge Correlation Feature |
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| Wei Benzheng,Gan Jie,Yin Yilong.Medical Image Registration Based on Mutual Information Entropy Combined with Edge Correlation Feature[J].Journal of Data Acquisition & Processing,2018,33(2):248-258. |
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| Authors: | Wei Benzheng Gan Jie Yin Yilong |
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| Affiliation: | 1. College of Science and Technology, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China;2. Computational Medicine Lab, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China;3. Department of Radiology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250001, China;4. School of Computer Science and Technology, Shandong University, Jinan, 250100, China |
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| Abstract: | Image registration is a valuable technique for medical diagnosis and treatment. Due to the inferiority of image registration using maximum mutual information, a new hybrid method of multimodality medical image registration based on mutual information of spatial information is proposed. The new measure that combines mutual information, spatial information and feature characteristics, is proposed. Edge points are used as features and obtained from a morphology gradient detector. Feature characteristics like location, edge strength and orientation are taken into account to compute a joint probability distribution of corresponding edge points in two images. Mutual information based on this function is minimized to find the best alignment parameters. Finally, the translation parameters are calculated by using a gradient descent algorithm. The experimental results demonstrate the high validation precision and excellent accelerating capability of the algorithm. |
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| Keywords: | image registration medical image mutual information entropy measure function edge feature |
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