基于点分布模型的3D模型拟合方法

3D model fitting method based on point distribution model

针对传统方法构建的患者病患部位的3D解剖结构模型与测试样本拟合效果较差的问题,提出基于点分布模型的3D模型拟合方法(3DMFB-PDM).对训练样本集进行数据处理,使模板样本与目标样本对齐,减小训练样本由于旋转及尺度变化产生的不利影响;在训练样本间建立对应关系,用正态分布表示患者的点分布模型;计算测试样本中的特征点与点分布模型中对应点间的距离,通过引入Mahalanobis距离和转换非线性方程组,使点间距最小.根据最小间距不断调整点分布模型的形状参数,使点分布模型与测试样本拟合.选取30组左侧股骨作为训练样本集实验验证3DMFB-PDM的有效性,结果表明引入附加项后的拟合误差小于未引入附加项的拟合误差.将3DMFB-PDM与其他3种方法进行对比,结果显示3DMFB-PDM的拟合误差最小,表明3DMFB-PDM能够有效地将患者病患部位的3D解剖结构模型与测试样本拟合.

A 3D model fitting method based on point distribution model(3DMFB-PDM)was proposed,aiming at the problem of poor fitting between the 3D anatomical structure model of the patient’s part constructed by the traditional method and the test samples.Firstly,the data processing was performed on the training sample set to align the template sample with the target sample,and the adverse effects of training sample due to rotation and scale changes were alleviated.Then the correspondence among training samples was established,and the patient’s point distribution model with a normal distribution was represented.Finally,the distance between the feature point and the corresponding point in the point distribution model was calculated.By introducing the Mahalanobis distance and converting the nonlinear equations were to minimize the distance.Meanwhile,the shape parameters of the point distribution model were continuously adjusted according to the minimum distance,thus the point distribution model was fitted to the test samples.30 groups of left femurs were selected as the training sample set for experimental verification to verify the effectiveness of 3DMFB-PDM.Results show that the fitting error after introducing additional terms is smaller than the fitting error without introducing additional terms.Comparing 3DMFB-PDM with the other three methods shows that the fitting error of 3DMFB-PDM is the smallest,which indicate that 3DMFBPDM can effectively fit the 3D anatomical structure model of the patient’s part to the test samples.