Automatic simultaneous segmentation and fast registration of histological images
Automatic simultaneous segmentation and fast registration of histological images
复制标题
组织学图像自动同步分割和快速配准
DOI：
10.1109/isbi.2014.6867985
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Jiri Borovec
中科院分区：
文献类型：
--
作者：
J. Kybic;Jiri Borovec
我们描述了一种自动快速配准具有不同外观的图像的方法。将图像联合分割为少量类，对分割后的图像进行配准，并重复该过程。该分割算法在超像素上计算特征向量，然后找到一个softmax分类器，使两幅图像中类标签之间的相互信息最大化。为了提高速度，注册考虑类之间接口上的矩形邻域的稀疏集。三角剖分是由边缘上的成对弹簧式项处理的空间正则化创建的。利用循环信念传播在全局范围内寻找最优变换。多分辨率有助于提高速度和鲁棒性。我们的主要应用是注册染色的组织学切片，这些切片很大，在局部和全局外观上都有所不同。我们表明，我们的方法具有与标准的基于像素的配准相当的精度，同时速度更快，更通用。
We describe an automatic method for fast registration of images with very different appearances. The images are jointly segmented into a small number of classes, the segmented images are registered, and the process is repeated. The segmentation calculates feature vectors on superpixels and then it finds a softmax classifier maximizing mutual information between class labels in the two images. For speed, the registration considers a sparse set of rectangular neighborhoods on the interfaces between classes. A triangulation is created with spatial regularization handled by pairwise spring-like terms on the edges. The optimal transformation is found globally using loopy belief propagation. Multiresolution helps to improve speed and robustness. Our main application is registering stained histological slices, which are large and differ both in the local and global appearance. We show that our method has comparable accuracy to standard pixel-based registration, while being faster and more general.