Finding available subclasses in high-dimensional medical databases using clustering techniques is considered as very important one in medical field. Due to similar intensi- ties between the datapoints in high-dimensio...Finding available subclasses in high-dimensional medical databases using clustering techniques is considered as very important one in medical field. Due to similar intensi- ties between the datapoints in high-dimensionality cancer medical database clustering techniques have failed to cluster the available subclasses with less error. Therefore this paper presents suitable fuzzy-based clustering techniques to find available subclasses in high-dimensional prostate and breast cancer databases. In addition this paper presents prototype initialization algorithm to avoid random initialization of initial prototypes. In order to evaluate the performance of proposed clustering techniques experimental study has been performed on benchmark databases. Finally the proposed methods have been successfully implemented to find the subclasses of cancers in prostate and breast cancer databases. The clustering results of proposed methods have been validated by evaluating clustering accuracy.展开更多
目的目前已有的单目视觉SLAM(simultaneous localization and mapping)系统每次开始运行时都将初始帧而不是绝对位置设置为参考帧,不能在一个固定的坐标系中获得位姿,导致无法重用已有的建图信息,而且在复杂场景中相机容易跟踪失败,需...目的目前已有的单目视觉SLAM(simultaneous localization and mapping)系统每次开始运行时都将初始帧而不是绝对位置设置为参考帧,不能在一个固定的坐标系中获得位姿,导致无法重用已有的建图信息,而且在复杂场景中相机容易跟踪失败,需要当前帧与已有的关键帧非常相似时才能重定位并继续建图。针对这个问题,提出一种具有重新初始化、地图重用与地图恢复能力的视觉SLAM系统。方法首先,加载先验地图,通过ORB(oriented brief)特征匹配SLAM系统当前帧与先验地图关键帧,并结合重定位方法完成SLAM系统的初始化。接着,为了避免丢失地图,建立一种应对SLAM系统跟踪失败的地图保存机制,保存跟踪成功地图,并提出一种自适应快速重新初始化算法,引入灭点检测,自动选择最佳重新初始化策略,保证SLAM系统继续跟踪与建图,建立的地图称为恢复地图。最后,对于跟踪成功地图与恢复地图,采用改进的回环方法获得它们之间的转换关系,并提出一种地图恢复法,减少跟踪成功地图与恢复地图尺度不一带来的误差,确保得到的全局一致地图更加准确。结果在经过加噪处理的KITTI数据集上进行地图恢复融合的测试,实验结果表明,在KITTI00、KITTI02、KITTI05数据集下,本文提出的SLAM系统比ORB-SLAM2系统分别可以多获得39.25%、47.75%、32.46%的地图信息。在EuRo C数据集上的运行结果表明,本文提出的单目视觉SLAM系统不仅在建图精度方面与ORB-SLAM2效果相当,还在跟踪稳定性方面有显著提升。结论本文提出的SLAM系统可以在跟踪失败的情况下有效恢复地图;此外,还可以高效重用SLAM系统已有的建图结果,固定SLAM地图坐标系,提升SLAM系统运行稳定性。展开更多
感兴趣区域在临床医学图像分析中占有重要地位.提出了一种基于单调推进曲线进化的感兴趣区域提取新方法.首先,通过极小化ROI(region of interest)能量函数,推导出区域速度函数项,并与基于边界的速度函数融合,提出融合ROI信息的单调推进S...感兴趣区域在临床医学图像分析中占有重要地位.提出了一种基于单调推进曲线进化的感兴趣区域提取新方法.首先,通过极小化ROI(region of interest)能量函数,推导出区域速度函数项,并与基于边界的速度函数融合,提出融合ROI信息的单调推进Snake模型.ROI信息能够增强曲线深入到对比度低且细窄的区域中的传播能力.其次,提出了多初始化快速推进算法,选择性地种植种子曲线有助于局部区域的生长从而进一步改善分割结果.此外,为提高计算效率,在多尺度空间进行数值求解,其中利用快速解传递方法实现粗一级尺度到细一级尺度解的传递,可以加速收敛.利用医学图像分割实验对该方法进行评估,结果表明:该方法能够快速、精确地提取低对比度和细窄的ROI区域.与现有方法相比,该方法的高效性同时体现在分割结果和计算代价上.展开更多
文摘Finding available subclasses in high-dimensional medical databases using clustering techniques is considered as very important one in medical field. Due to similar intensi- ties between the datapoints in high-dimensionality cancer medical database clustering techniques have failed to cluster the available subclasses with less error. Therefore this paper presents suitable fuzzy-based clustering techniques to find available subclasses in high-dimensional prostate and breast cancer databases. In addition this paper presents prototype initialization algorithm to avoid random initialization of initial prototypes. In order to evaluate the performance of proposed clustering techniques experimental study has been performed on benchmark databases. Finally the proposed methods have been successfully implemented to find the subclasses of cancers in prostate and breast cancer databases. The clustering results of proposed methods have been validated by evaluating clustering accuracy.
文摘感兴趣区域在临床医学图像分析中占有重要地位.提出了一种基于单调推进曲线进化的感兴趣区域提取新方法.首先,通过极小化ROI(region of interest)能量函数,推导出区域速度函数项,并与基于边界的速度函数融合,提出融合ROI信息的单调推进Snake模型.ROI信息能够增强曲线深入到对比度低且细窄的区域中的传播能力.其次,提出了多初始化快速推进算法,选择性地种植种子曲线有助于局部区域的生长从而进一步改善分割结果.此外,为提高计算效率,在多尺度空间进行数值求解,其中利用快速解传递方法实现粗一级尺度到细一级尺度解的传递,可以加速收敛.利用医学图像分割实验对该方法进行评估,结果表明:该方法能够快速、精确地提取低对比度和细窄的ROI区域.与现有方法相比,该方法的高效性同时体现在分割结果和计算代价上.
