Mallick Satya P, Zhu Yuanxin, Kriegman David
Department of Electrical and Computer Engineering, University of California at San Diego, La Jolla, CA 92093, USA.
J Struct Biol. 2004 Jan-Feb;145(1-2):52-62. doi: 10.1016/j.jsb.2003.11.005.
A new learning-based approach is presented for particle detection in cryo-electron micrographs using the Adaboost learning algorithm. The approach builds directly on the successful detectors developed for the domain of face detection. It is a discriminative algorithm which learns important features of the particle's appearance using a set of training examples of the particles and a set of images that do not contain particles. The algorithm is fast (10 s on a 1.3 GHz Pentium M processor), is generic, and is not limited to any particular shape or size of the particle to be detected. The method has been evaluated on a publicly available dataset of 82 cryoEM images of keyhole lympet hemocyanin (KLH). From 998 automatically extracted particle images, the 3-D structure of KLH has been reconstructed at a resolution of 23.2 A which is the same resolution as obtained using particles manually selected by a trained user.
提出了一种基于学习的新方法，用于使用Adaboost学习算法在冷冻电子显微镜图像中进行颗粒检测。该方法直接基于为面部检测领域开发的成功检测器。它是一种判别算法，使用一组颗粒的训练示例和一组不包含颗粒的图像来学习颗粒外观的重要特征。该算法速度快（在1.3 GHz奔腾M处理器上需10秒），具有通用性，并且不限于要检测的颗粒的任何特定形状或大小。该方法已在公开可用的数据集上进行了评估，该数据集包含82张钥孔戚血蓝蛋白（KLH）的冷冻电镜图像。从998个自动提取的颗粒图像中，已以23.2埃的分辨率重建了KLH的三维结构，该分辨率与由训练有素的用户手动选择的颗粒所获得的分辨率相同。
