超声图像中基于经验模态分解自动提取内-中膜厚度

目的为改善传统人工标记测量血管内-中膜厚度(IMT)的准确性和稳定性,提出基于图像分割技术的经验模态分解(EMD)改进算法。方法采用EMD改进算法去噪,根据血管壁的特点,在其中的极值点插值步骤使用非均匀的二维B样条函数,在水平和垂直方向上控制网格的密度不同,分别满足不同的分辨精度和平滑程度要求,改进了原始的二维EMD算法;然后通过K均值方法从图像中分离出血管腔、血管壁和其他组织,使用数学形态学算法逐步得到最终的内-中膜组织分割结果。结果改进EMD算法取得了较好的重建和滤波效果,有效克服了超声图像的强噪声和低分辨力对图像分割的限制,整个算法分割比较准确,算法复杂度相对较小。结论改进EMD算法是在超声图像中自动提取内-中膜的较有潜力的方法,能有效去除超声噪声,同时保留条纹结构的细节和边缘信息,有望于其他强噪声环境下提取条纹结构。

Automatic extraction of intima-media-thickness on ultrasonography based on empirical mode decomposition

Objective This study introduces an improved empirical mode decomposition (EMD) algorithm for measuring intima medial thickness (IMT) based on image segmentation techniques, in order to improve the accuracy and stability of traditional manual measurement of IMT. Methods An improved EMD algorithm is proposed for noise reduction, in which the control lattice densities of the bi-dimensional B-splines adopted in the interpolation step are different in horizontal and vertical orientations. The modification is based on the fact that, in the original ultrasound images, the requirements of precision and smoothness in two directions are different. K-means algorithm is then used to separate the lumen, vessel wall and other structures, followed by mathematical morphological methods used for obtaining the final segmentation of intima-media. Results The improved EMD algorithm performed well in denoising and reconstructing the image, which effectively overcomed the challenges in carotid ultrasound image segmentation with due to heavy noise and low-resolution. The results showed that the proposed segmentation method had achieved a good accuracy witch much lower complexity comparing to the methods based on deformable models.Conclusion The improved EMD algorithm can effectively remove the ultrasonic noise while preserving details and edges of the structure, and therefore has great potential for automatic extraction of IMT in carotid ultrasound images. It should also be applicable to extract stripe structures under heavy noise environment according to the principles of the method.