...
首页> 外文期刊>IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics >Robust segmentation of tubular structures in 3-D medical images by parametric object detection and tracking
【24h】

Robust segmentation of tubular structures in 3-D medical images by parametric object detection and tracking

机译:通过参数对象检测和跟踪对3D医学图像中的管状结构进行稳健的分割

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

We present a novel approach to the coarse segmentation of tubular structures in three-dimensional (3-D) image data. Our algorithm, which requires only few initial values and minimal user interaction, can be used to initialize complex deformable models and is based on an extension of the randomized hough transform (RHT), a robust method for low-dimensional parametric object detection. Tubular structures are modeled as generalized cylinders. By means of a discrete Kalman filter, they are tracked through 3-D space. Our extensions to the RHT are a feature adaptive selection of the sample size, expectation-dependent weighting of the input data, and a novel 3-D parameterization for straight elliptical cylinders. Experimental results obtained for 3-D synthetic as well as for 3-D medical images demonstrate the robustness of our approach w.r.t. image noise. We present the successful segmentation of tubular anatomical structures such as the aortic arc and the spinal cord.
机译:我们提出了一种新颖的方法来对三维(3-D)图像数据中的管状结构进行粗略分割。我们的算法只需要很少的初始值和最少的用户交互,就可以用于初始化复杂的可变形模型,并且基于随机霍夫变换(RHT)的扩展,这是一种用于低维参数化对象检测的可靠方法。管状结构被建模为广义圆柱。通过离散卡尔曼滤波器,可以在3D空间中跟踪它们。我们对RHT的扩展包括:对样本大小的自适应选择,对输入数据的期望相关加权以及对直椭圆形圆柱体的新颖3D参数化。针对3-D合成图像和3-D医学图像获得的实验结果证明了我们的方法的鲁棒性图像噪点。我们目前成功地分割了管状解剖结构,例如主动脉弧和脊髓。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号