A new algorithm for segmentation of suspected lung ROI(regions of interest)by mean-shift clustering and multi-scale HESSIAN matrix dot filtering was proposed.Original image was firstly filtered by multi-scale HESSIAN matrix dot filters,round suspected nodular lesions in the image were enhanced,and linear shape regions of the trachea and vascular were suppressed.Then,three types of information,such as,shape filtering value of HESSIAN matrix,gray value,and spatial location,were introduced to feature space.The kernel function of mean-shift clustering was divided into product form of three kinds of kernel functions corresponding to the three feature information.Finally,bandwidths were calculated adaptively to determine the bandwidth of each suspected area,and they were used in mean-shift clustering segmentation.Experimental results show that by the introduction of HESSIAN matrix of dot filtering information to mean-shift clustering,nodular regions can be segmented from blood vessels,trachea,or cross regions connected to the nodule,non-nodular areas can be removed from ROIs properly,and ground glass object(GGO)nodular areas can also be segmented.For the experimental data set of 127 different forms of nodules,the average accuracy of the proposed algorithm is more than 90%.
Accurate and automatic segmentation of airway tree from multi-slice computed tomography（MSCT） chest scan is an...
Wenjun Tan 1 , Jinzhu Yang 1 , Dazhe Zhao 1 , Shuang Ma 2 Li Qu 2 Jinchi Wang 2 1. Medical Image Computing Laboratory of Ministry of Education, Northeastern University, Shenyang 1108192. College of Information Science and Engineering, Northeastern University, Shenyang 110819
提出一种基于改进BET(brain extraction tool)的MRI脑组织自动提取算法.首先,该算法结合图像梯度信息能够估计出更为准确的脑重心(center of gravity,COG);其次,该算法构建了新的脑表面形变力,在垂直于脑表面切线的扩张力中引入了边缘力,该力很好地抑制了脑组织的边界泄漏和过度分割问题.使用本文方法对MRI脑影像进行了自动脑组织提取,实验结果表明,本文算法能够自动获得更加准确的脑组织提取结果,特别是在脑组织边缘处,本文算法与BET算法相比,提取结果更准确.
