对表面法向量投影叠加法进行改进,提出一种基于三维局部形状约束并采用自适应投影距离的表面法向量叠加疑似肺结节检测方法。现有的表面法向量投影叠加法没有对初始ROI(region of interest)区域表面进行形状约束,采用单一投影距离,存在...对表面法向量投影叠加法进行改进,提出一种基于三维局部形状约束并采用自适应投影距离的表面法向量叠加疑似肺结节检测方法。现有的表面法向量投影叠加法没有对初始ROI(region of interest)区域表面进行形状约束,采用单一投影距离,存在投影叠加计算量大且只适用于固定大小的肺结节检测问题。首先采用Otsu阈值方法得到初始ROI区域,计算初始ROI曲面的三维局部形状指数,对三维ROI的体素表面法向量进行投影约束,以提高球形选择性、减少投影叠加计算量;在表面法向量叠加过程中由ROI曲面自适应地决定投影距离,一方面限制表面法向量向ROI曲面外投影;另一方面可以克服检测固定大小肺结节的局限性;由于肺结节一般表现为球形,具有较大的表面法向量投影叠加值,选择局部最大叠加值,可以检测不同大小的球形疑似肺结节区域。实验结果表明,改进算法具有更好的球形选择性,可以较好地检测出不同大小的疑似肺结节,具有较高的敏感度和较低的假阳性率。展开更多
Based on the empirical or theoretical qualitative information about the relationship between response variable and covariates, we propose a new approach to model polynomial regression using a shape restricted regressi...Based on the empirical or theoretical qualitative information about the relationship between response variable and covariates, we propose a new approach to model polynomial regression using a shape restricted regression after estimating the direction by sufficient dimension reduction. The purpose of this paper is to illustrate that in the absence of prior information other than the shape constraints, our approach provides a flexible fit to the data and improves regression predictions. We use central subspace to estimate the directions and fit a final model by shape restricted regression, when the shape is known or is stipulated from empirical inspection. Comparisons with an alternative nonparametric regression are included. Simulated and real data analyses are conducted to illustrate the performance of our approach.展开更多
文摘对表面法向量投影叠加法进行改进,提出一种基于三维局部形状约束并采用自适应投影距离的表面法向量叠加疑似肺结节检测方法。现有的表面法向量投影叠加法没有对初始ROI(region of interest)区域表面进行形状约束,采用单一投影距离,存在投影叠加计算量大且只适用于固定大小的肺结节检测问题。首先采用Otsu阈值方法得到初始ROI区域,计算初始ROI曲面的三维局部形状指数,对三维ROI的体素表面法向量进行投影约束,以提高球形选择性、减少投影叠加计算量;在表面法向量叠加过程中由ROI曲面自适应地决定投影距离,一方面限制表面法向量向ROI曲面外投影;另一方面可以克服检测固定大小肺结节的局限性;由于肺结节一般表现为球形,具有较大的表面法向量投影叠加值,选择局部最大叠加值,可以检测不同大小的球形疑似肺结节区域。实验结果表明,改进算法具有更好的球形选择性,可以较好地检测出不同大小的疑似肺结节,具有较高的敏感度和较低的假阳性率。
文摘Based on the empirical or theoretical qualitative information about the relationship between response variable and covariates, we propose a new approach to model polynomial regression using a shape restricted regression after estimating the direction by sufficient dimension reduction. The purpose of this paper is to illustrate that in the absence of prior information other than the shape constraints, our approach provides a flexible fit to the data and improves regression predictions. We use central subspace to estimate the directions and fit a final model by shape restricted regression, when the shape is known or is stipulated from empirical inspection. Comparisons with an alternative nonparametric regression are included. Simulated and real data analyses are conducted to illustrate the performance of our approach.
