Clifford代数空间上的三维颅部感兴趣区配准
发布时间：2018-08-09 14:40
【摘要】：针对3D颅部医学图像配准中存在的配准精度不高、运算复杂、配准效率低等问题,在创新性地圈定了感兴趣配准区域的基础上,提出了一种基于Clifford代数的全新的几何特征轴构造方法。起初从参考模态与浮动模态中依次提取特征点,通过该特征点实现配准感兴趣区(ROI)圈定;其次利用感兴趣区的点云数据集到其质心的距离测度构造几何特征轴,并计算相应的旋转算子完成浮动模态相对于参考模态的高效、高精度配准。这样的配准方式有效地避免了多模态图像成像时配准区域非完全匹配导致的误差,并减少待处理的数据量,同时消除了无效配准区域产生的局部最优点的影响,进而降低了配准的误差。实验表明,感兴趣区处理后的待配准图像,经新算法仿真配准,能够精确地定位组织器官的三维位置,执行效率高且配准误差较小,是一种有效的3D颅部医学图像配准方法。
[Abstract]:Aiming at the problems existing in 3D cranial medical image registration, such as low registration accuracy, complicated operation and low registration efficiency, the region of interest is delineated innovatively. A new method of constructing geometric feature axis based on Clifford algebra is proposed. At first, the feature points are extracted from the reference mode and floating mode in turn, and the region of interest (ROI) is registered by this feature point. Secondly, the geometric feature axis is constructed by using the distance measure from the point cloud data set of the region of interest to its centroid. The corresponding rotation operator is calculated to achieve the high efficiency and high precision registration of the floating mode relative to the reference mode. This registration method can effectively avoid the error caused by incomplete matching of registration region in multi-mode image imaging, reduce the amount of data to be processed, and eliminate the influence of local optimal points caused by invalid registration region. Furthermore, the error of registration is reduced. The experimental results show that the new algorithm can accurately locate the 3D position of tissue and organ, and the efficiency of the algorithm is high and the registration error is small. It is an effective method for the registration of 3D cranial medical images.
【作者单位】： 南通大学电气工程学院;
【基金】：国家自然科学基金(61273024,61305031) 江苏省自然科学基金(BY2016053-11) 江苏省“333”高层次人才培养工程(BRA2015366) 江苏省优势学科(PAPD)资助
【分类号】：R741;TP391.41
，
本文编号：2174400
[Abstract]:Aiming at the problems existing in 3D cranial medical image registration, such as low registration accuracy, complicated operation and low registration efficiency, the region of interest is delineated innovatively. A new method of constructing geometric feature axis based on Clifford algebra is proposed. At first, the feature points are extracted from the reference mode and floating mode in turn, and the region of interest (ROI) is registered by this feature point. Secondly, the geometric feature axis is constructed by using the distance measure from the point cloud data set of the region of interest to its centroid. The corresponding rotation operator is calculated to achieve the high efficiency and high precision registration of the floating mode relative to the reference mode. This registration method can effectively avoid the error caused by incomplete matching of registration region in multi-mode image imaging, reduce the amount of data to be processed, and eliminate the influence of local optimal points caused by invalid registration region. Furthermore, the error of registration is reduced. The experimental results show that the new algorithm can accurately locate the 3D position of tissue and organ, and the efficiency of the algorithm is high and the registration error is small. It is an effective method for the registration of 3D cranial medical images.
【作者单位】： 南通大学电气工程学院;
【基金】：国家自然科学基金(61273024,61305031) 江苏省自然科学基金(BY2016053-11) 江苏省“333”高层次人才培养工程(BRA2015366) 江苏省优势学科(PAPD)资助
【分类号】：R741;TP391.41
，
本文编号：2174400
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