Multi-modal image registration using edge neighbourhood descriptor
Multi-modal image registration using edge neighbourhood descriptor
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DOI：
10.1049/el.2014.0795
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发表时间：
2014-05
影响因子：
1.1
通讯作者：
Zhulou Cao;Enqing Dong
中科院分区：
文献类型：
--
作者：
Zhulou Cao;Enqing Dong
多模态医学图像配准由于难以处理强度失真问题而一直是一个具有挑战性和活跃的研究领域。最近提出了基于局部块的熵图像来解决这个问题。然而，这种方法的缺点是计算熵的复杂性。提出了一种新的图像稠密描述子--边缘邻域描述子，它对图像的不同区域，即图像背景、图像边缘、边缘邻域和剩余区域赋予不同的值。边缘邻域描述子比熵图像更快。实验结果表明，与熵图像和互信息方法相比，该描述子能提高配准精度。
Multi-modal medical image registration remains a challenging and active area of research owing to the difficulty of dealing with intensity distortions. Local patch-based entropy images were recently proposed to address this problem. However, this method suffers from the complexity of calculation of the entropy. Proposed is a new dense descriptor for an image, named the edge neighbourhood descriptor, which assigns different values to different image regions, namely the image background, image edges, edge neighbourhoods and the remaining area. The edge neighbourhood descriptor is faster than the entropy image. Experimental results show that this new descriptor can improve registration accuracy compared with the results of the entropy image and the mutual information method.