Ward Aaron D, Hamarneh Ghassan
Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada.
Med Image Comput Comput Assist Interv. 2007;10(Pt 1):278-85. doi: 10.1007/978-3-540-75757-3_34.
We propose a highly automated approach to the point correspondence problem for anatomical shapes in medical images. Manual landmarking is performed on a small subset of the shapes in the study, and a machine learning approach is used to elucidate the characteristic shape and appearance features at each landmark. A classifier trained using these features defines a cost function that drives key landmarks to anatomically meaningful locations after MDL-based correspondence establishment. Results are shown for artificial examples as well as real data.
我们提出了一种高度自动化的方法来解决医学图像中解剖形状的点对应问题。在研究中的一小部分形状上进行手动地标标注，并使用机器学习方法来阐明每个地标处的特征形状和外观特征。使用这些特征训练的分类器定义了一个成本函数，该函数在基于最小描述长度（MDL）的对应关系建立后，将关键地标驱动到解剖学上有意义的位置。展示了人工示例以及真实数据的结果。
