最小能量约束与ARDP算法混合的病灶点云重建

发布时间：2018-09-07 13:01

【摘要】：针对传统面绘制方法随真实感的提升效率急剧下降,且交互性及灵敏度较差的问题,基于CT点云数据提出了一种肝脏病灶的表面重建方法。首先改进了点云数据的插值和自适应精简方法;然后提出将模型重构过程分为两部分,先通过最小能量约束和简化的MC算法由点云距离场快速创建粗糙的基底模型,接着提出一种线性最优化的ARDP算法用于自动计算点元投影向量,从而将当前模型表面节点直接映射至点云,通过交互式地确定迭代次数可按需逐步提高模型精确度,最终获取高质量模型,实现散乱点到平滑面的直接过渡。实验结果表明,利用该算法生成平均误差小于0.000 1的高精模型将大大缩短时间,且对不规则病灶模型有着良好的适应性。
[Abstract]:In order to solve the problem that the efficiency of traditional surface rendering method decreases sharply with the improvement of reality, and the interaction and sensitivity are poor, a surface reconstruction method for liver lesions is proposed based on CT point cloud data. Firstly, the interpolation and adaptive reduction of point cloud data are improved, and then the reconstruction process of the model is divided into two parts. Firstly, the rough base model is created by the minimum energy constraint and the simplified MC algorithm from the point cloud distance field. Then a linear optimization ARDP algorithm is proposed to calculate the projection vector of point element automatically, so that the surface nodes of the current model can be mapped directly to the point cloud, and the accuracy of the model can be improved step by interactively determining the number of iterations. Finally, a high quality model is obtained to realize the direct transition from scattered points to smooth surfaces. The experimental results show that the algorithm can greatly shorten the time and has a good adaptability to the irregular focus model by generating a high-precision model with an average error of less than 0.000 1.
【作者单位】： 福州大学物理与信息工程学院;
【基金】：国家自然科学基金(No.61471124) 福建省自然科学基金(No.2013J05090) 福建省科技计划重点项目(No.2011H0027)
【分类号】：R575;R816.5;TP391.41
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本文编号：2228336