Abstract

The vessel structures of the blood circulatory system are one of the most complex structures of the human body. Modern computed tomography techniques allow acquiring high resolution images, but at the same time, the number of artifacts in output images is quite high. They may affect diagnostic result and may obscure or simulate pathology. The idea of our method is to represent a 3D computed tomography image as a combination of vascular structure and background that has normal distribution in some neighborhood. Locally adaptive non-linear filters decrease global difference between bright and dark voxels, even if it produces better local contrast. Luminosity and contrast are observed from image background and are used for normalization of the whole image. After making background normalization at each layer, we merge layers and reconstruct vessels structure. The proposed method has been tested on real cardiac CT images, the test results show that high quality 3D structures are reconstructed, without requiring a priori knowledge or user interaction. The tested dataset has been made publicly available. The proposed approach can be applied to denoising computed tomography images, enhancing of contrast in lesion areas without changing topology of initial vessel structures.

中文翻译：

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通过对象/背景归一化提取3D心脏CT图像中的血管结构

摘要

血液循环系统的血管结构是人体最复杂的结构之一。现代计算机断层扫描技术允许获取高分辨率图像，但同时，输出图像中的伪像数量也很高。它们可能会影响诊断结果，并可能掩盖或模拟病理。我们方法的想法是将3D计算机断层扫描图像表示为在某些邻域具有正态分布的血管结构和背景的组合。局部自适应非线性滤镜可减少亮体素和暗体素之间的全局差异，即使它产生更好的局部对比度也是如此。从图像背景观察到亮度和对比度，并将其用于整个图像的标准化。在每一层进行背景归一化后，我们合并图层并重建血管结构。所提出的方法已在真实的心脏CT图像上进行了测试，测试结果表明，无需先验知识或用户交互即可重建高质量3D结构。经测试的数据集已公开可用。所提出的方法可以应用于对计算机断层摄影图像进行去噪，在不改变初始血管结构拓扑的情况下增强病变区域的对比度。