Abstract：Along with the rapid development of imaging techniques for small animals, more and more images obtained from small animals need to be analyzed per day, therefore automated image analysis method has become an urgent demand. For mice images, the significant inter-subject posture variations become a major difficulty for automated image analysis. In this paper, an automatic bone joint localization method was developed for mouse micro-CT images, so as to help with posture identification of mouse body. The proposed method was composed of three steps: (1) classification random forests for rough joint localization, (2) aggregating the results of classification through regression forest, and (3) picking up landmarks in the right position by the mapping graph. The method achieved automatic bone joint localization for 49 test images of different body postures. The median localization error of the whole body CT images was 0.68 mm. The success rate of localization was 98.27%. We also demonstrated the necessity of combining classification and regression random forest and discussed the influence on localization with different number of training data. With this new method for mouse micro-CT posture identification was expected to provide helpful information for the subsequent image registration, segmentation and measurements.
屠睿博, 陈中华, 王洪凯. 基于随机森林算法的小鼠micro-CT影像中骨骼关节特征点定位[J]. 中国生物医学工程学报, 2017, 36(3): 257-266.
Tu Ruibo, Chen Zhonghua, Wang Hongkai. Bone Joints Localization in Mouse Micro-CT Images Using Random Forests Algorithm. Chinese Journal of Biomedical Engineering, 2017, 36(3): 257-266.
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