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目的针对现有的血管分割方法对血管的分割精度尚有不足,尤其是对噪声等影响下的断裂血管,基于Stein-Weiss函数的解析性提出了一种新的3维血管分割算法,能够分割出更精细更清晰的血管。方法首先,通过图像增强和窗宽窗位调节的预处理来增加血管点与背景的对比度。然后,将Stein-Weiss函数与梯度算子结合起来,把CT体数据的每一个体素都表示为一个Stein-Weiss函数,体素6邻域的灰度值作为Stein-Weiss函数各组成部分的系数。再求出Stein-Weiss函数在x、y、z 3个方向上的梯度值,大于某一个阈值时,便将此体素视为血管边缘上的点。最后,根据提取出血管边缘的2维CT切片重建出3维的血管。结果对肝静脉的造影数据S70进行肝脏血管分割与3维重建的实验结果表明,利用该算法进行血管分割的敏感性和特异性相对于区域生长算法和八元数解析分割算法都较高。尤其是对于血管分割的去噪方面有明显优势,因此能够快速有效地分割出更清晰更精细的血管。结论提出了一种新的血管分割算法,利用Stein-Weiss函数的解析性来提取血管的边缘,实验结果表明,此算法可以有效快速地去除血管噪声并得到更精细的分割结果。由于Stein-Weiss解析的性质可以适合任意维数,所以利用Stein-Weiss解析函数性质可以进行2维或更高维的图像边缘识别。
Aim To solve the problem that the existing methods of vessel segmentation have poor segmentation accuracy, especially for the broken vessels under the influence of noise, a new 3-dimensional segmentation algorithm based on Stein-Weiss function is proposed. A finer and clearer blood vessel. Methods First, the contrast between the blood vessel point and the background is increased by image preprocessing with image enhancement and window wide window level adjustment. Then, the Stein-Weiss function is combined with the gradient operator to represent each voxel of the CT volume data as a Stein-Weiss function and the gray value of the neighborhood of the voxel 6 as a constituent of the Stein-Weiss function coefficient. The Stein-Weiss function is then used to determine the gradient in the x, y, and z directions. If it is greater than a threshold, the voxel is treated as a point on the edge of the blood vessel. Finally, three-dimensional blood vessels were reconstructed from the 2-dimensional CT slices extracted from the edge of the blood vessel. Results The experimental results of hepatic vascular segmentation and 3D reconstruction of hepatic venous angiography data S70 showed that the sensitivity and specificity of this algorithm for vascular segmentation were higher than that of region growing algorithm and octave analysis and segmentation algorithm. Especially for the vascularization of the denoising has obvious advantages, it can quickly and effectively cut out a clearer and more sophisticated blood vessels. Conclusion A new algorithm for vessel segmentation is proposed. The edge of the vessel is extracted by the analyticality of Stein-Weiss function. The experimental results show that this algorithm can remove the vessel noise effectively and get a finer segmentation result. Because the properties of Stein-Weiss analysis can fit any dimension, Stein-Weiss can be used to analyze image edge recognition in 2D or higher dimensions.