融合加权随机森林的自动3D椎骨CT图像主动轮廓分割方法  

Automatic 3D vertebrae CT image active contour segmentation method based on weighted random forest

作　　者：刘侠[1] 甘权[1] 李冰[1] 刘晓 王波 Liu Xia;Gan Quan;Li Bing;Liu Xiao;Wang Bo(School of Automation,Harbin University of Science and Technology,Harbin,Heilongjiang 150080,China)

机构地区：[1]哈尔滨理工大学自动化学院,黑龙江哈尔滨150080

出　　处：《光电工程》2020年第12期35-46,共12页Opto-Electronic Engineering

基　　金：国家自然科学基金资助项目(61172167);黑龙江省自然科学基金资助项目(QC2017076)。

摘　　要：为了解决CT图像主动轮廓分割方法对初始轮廓的敏感和分割不准确的问题,本文提出一种融合加权随机森林的自动3D椎骨CT主动轮廓分割方法WRF-AC。该方法提出加权随机森林算法和包含边缘能量的主动轮廓能量函数。首先,通过提取椎骨CT的3D Haar-like特征值训练加权随机森林获得的椎骨中心作为分割的初始轮廓,然后,求解包含边缘能量的主动轮廓能量函数最小值完成椎骨CT图像的分割。实验结果表明,本方法在相同数据集上能够更加准确、快速地分割脊柱CT图像提取椎骨部分。In order to solve the problems of sensitive initial contours and inaccurate segmentation caused by active contour segmentation of CT images,this paper proposes an automatic 3D vertebral CT active contour segmentation method combined weighted random forest called“WRF-AC”.This method proposes a weighted random forest algorithm and an active contour energy function that includes edge energy.First,the weighted random forest is trained by extracting 3D Haar-like feature values of the vertebra CT,and the'vertebra center'obtained is used as the initial contour of the segmentation.Then,the segmentation of the vertebra CT image is completed by solving the active contour energy function minimum containing the edge energy.The experimental results show that this method can segment the spine CT images more accurately and quickly on the same datasets to extract the vertebrae.

关 键 词：3D分割 CT图像 加权随机森林 主动轮廓 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]