Vascular segmentation is often required in medical image analysis for various imaging modalities. Despite the rich literature in the field, the proposed methods need most of the time adaptation to the particular investigation and may sometimes lack the desired accuracy in terms of true positive and false positive detection rate. This paper proposes a general method for vascular segmentation based on locally connected filtering applied in a multiresolution scheme. The filtering scheme performs progressive detection and removal of the vessels from the image relief at each resolution level, by combining directional 2D-3D locally connected filters (LCF). An important property of the LCF is that it preserves (positive contrasted) structures in the image if they are topologically connected with other similar structures in their local environment. Vessels, which appear as curvilinear structures, can be filtered out by an appropriate LCF set-up which will minimally affect sheet-like structures. The implementation in a multiresolution framework allows dealing with different vessel sizes. The outcome of the proposed approach is illustrated on several image modalities including lung, liver and coronary arteries. It is shown that besides preserving high accuracy in detecting small vessels, the proposed technique is less sensitive with respect to noise and the presence of pathologies of positive-contrast appearance on the images. The detection accuracy is compared with a previously developed approach on the 20 patient database from the VESSEL12 challenge.

在医学图像分析中，通常需要针对各种成像方式进行血管分割。尽管在该领域有丰富的文献，但是所提出的方法大部分时间都需要适应特定的研究，并且有时在真阳性和假阳性检出率方面可能缺乏所需的准确性。本文提出了一种在多分辨率方案中应用的基于局部连接滤波的常规血管分割方法。通过组合定向2D-3D局部连接的滤镜（LCF），该滤镜方案在每个分辨率级别上对图像浮雕执行渐进式检测和去除。LCF的一个重要特性是，如果拓扑结构与本地环境中的其他类似结构拓扑连接，则它会保留图像中的（正对比）结构。可以通过适当的LCF设置将出现为曲线结构的船只过滤掉，这将对板状结构的影响降到最低。在多分辨率框架中的实现允许处理不同的容器尺寸。提议的方法的结果在包括肺，肝和冠状动脉在内的几种图像模式中得到了说明。结果表明，除了在检测小血管方面保持高精度外，所提出的技术对噪声和图像上出现正对比的病态的敏感性较低。将检测准确性与VESSEL12挑战中先前针对20位患者数据库开发的方法进行了比较。在多分辨率框架中的实现允许处理不同的容器尺寸。提议的方法的结果在包括肺，肝和冠状动脉在内的几种图像模式中得到了说明。结果表明，除了在检测小血管方面保持高精度外，所提出的技术对噪声和图像上出现正对比的病态的敏感性较低。将检测准确性与VESSEL12挑战中先前针对20位患者数据库开发的方法进行了比较。在多分辨率框架中的实现允许处理不同的容器尺寸。提议的方法的结果在包括肺，肝和冠状动脉在内的几种图像模式中得到了说明。结果表明，除了在检测小血管方面保持高精度外，所提出的技术对噪声和图像上出现正对比的病态的敏感性较低。将检测准确性与VESSEL12挑战中先前针对20位患者数据库开发的方法进行了比较。结果表明，除了在检测小血管方面保持高精度外，所提出的技术对噪声和图像上出现正对比的病态的敏感性较低。将检测准确度与先前针对VESSEL12挑战在20位患者数据库中开发的方法进行比较。结果表明，除了在检测小血管方面保持高精度外，所提出的技术对噪声和图像上出现正对比的病态的敏感性较低。将检测准确性与VESSEL12挑战中先前针对20位患者数据库开发的方法进行了比较。