基于深度卷积特征光流的形变医学图像配准算法

光流法是一种基于光流场模型的重要而有效的形变配准算法。针对现有光流法所用特征质量不高使得配准结果不够准确的问题,将深度卷积神经网络特征和光流法相结合,提出了基于深度卷积特征光流(DCFOF)的形变医学图像配准算法。首先利用深度卷积神经网络稠密地提取图像中每个像素所在图像块的深度卷积特征,然后基于固定图像和浮动图像间的深度卷积特征差异求解光流场。通过提取图像的更为精确和鲁棒的深度学习特征,使求得的光流场更接近真实形变场,提升了配准精度。实验结果表明,所提算法能够更有效地解决形变医学图像配准问题,其配准精度优于Demons算法、尺度不变特征变换(SIFT)Flow算法以及医学图像专业配准软件Elastix。

Deformable medical image registration algorithm based on deep convolution feature optical flow

Optical flow method is an important and effective deformation registration algorithm based on optical flow field model. Aiming at the problem that the feature quality used by the existing optical flow method is not high enough to make the registration result accurate, combining the features of deep convolutional neural network and optical flow method, a deformable medical image registration algorithm based on Deep Convolution Feature Based Optical Flow (DCFOF) was proposed. Firstly, the deep convolution feature of the image block where each pixel in the image was located was densely extracted by using a deep convolutional neural network, and then the optical flow field was solved based on the deep convolution feature difference between the fixed image and the floating image. By extracting more accurate and robust deep learning features of the image, the optical flow field obtained was closer to the real deformation field, and the registration accuracy was improved. Experimental results show that the proposed algorithm can solve the problem of deformable medical image registration effectively, and has the registration accuracy better than those of Demons algorithm, Scale-Invariant Feature Transform（SIFT） Flow algorithm and professional registration software of medical images called Elastix.