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首页> 外文会议>Visualization, Image-Guided Procedures pt.2; Progress in Biomedical Optics and Imaging; vol.8 no.28; Proceedings of SPIE-The International Society for Optical Engineering; vol.6509 pt.2 >Robust Centerline Extraction from Tubular Structures in Medical Images
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Robust Centerline Extraction from Tubular Structures in Medical Images

机译：从医学图像中的管状结构中可靠地提取中心线

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摘要

Extraction of centerlines is useful to analyzing objects in medical images, such as lung, bronchia, blood vessels, and colon. Given the noise and other imaging artifacts that are present in medical images, it is crucial to use robust algorithms that are (1) noise tolerant, (2) computationally efficient, (3) accurate and (4) preferably, do not require an accurate segmentation and can directly operate on grayscale data. We propose a new centerline extraction method that employs a Gaussian type probability model to build a more robust distance field. The model is computed using an integration of the image gradient field, in order to estimate boundaries of interest. Probabilities assigned to boundary voxels are then used to compute a modified distance field. Standard distance field algorithms are then applied to extract the centerline. We illustrate the accuracy and robustness of our algorithm on a synthetically generated example volume and a radiologist supervised segmented head MRT angiography dataset with significant amounts of Gaussian noise, as well as on three publicly available medical volume datasets. Comparison to traditional distance field algorithms is also presented.
机译：中心线的提取对于分析医学图像中的对象（例如肺，支气管，血管和结肠）很有用。给定医学图像中存在的噪声和其他成像伪影，至关重要的是使用可靠的算法，这些算法必须（1）耐噪声，（2）计算效率高，（3）准确并且（4）优选不需要精确分割，并且可以直接对灰度数据进行操作。我们提出了一种新的中心线提取方法，该方法采用高斯类型概率模型来构建更鲁棒的距离场。为了估计感兴趣的边界，使用图像梯度场的积分来计算模型。然后，将分配给边界体素的概率用于计算修改后的距离场。然后应用标准距离场算法提取中心线。我们在合成生成的示例体积和放射科医生监督的具有大量高斯噪声的分段头部MRT血管造影数据集以及三个可公开获得的医疗体积数据集上说明了我们算法的准确性和鲁棒性。还提出了与传统距离场算法的比较。

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