改进的基于单一尺度的医学图像边缘检测

Improved Single Scale Edge Detection for Medical Image

针对经典取边缘算法的缺点和高斯多尺度边缘检测中尺度选择的复杂性等问题,提出了改进的单一尺度边缘检测方法,并将该方法应用到医学图像的边缘检测中。该方法首先用平滑理论,对图像进行平滑,将图像中一些无用的细节信息平滑掉,抑制噪声和高频干扰成分;因为边缘细节也被平滑掉,所以再利用模糊增强算子加大边缘两侧灰度的差异,然后利用基于高斯核的单一尺度过零点边缘检测方法提取图像的边缘;最后,将该算法与经典的sobel,canny算子进行比较。实验结果表明,这种方法较好解决了图像边缘的提取精度和图像噪声的抑制能力之间的矛盾。

Aiming at the shortcoming of the classical edge algorithm and the complexity of the Gaussian multi-scale edge detection, this paper proposed an improved single scale edge detection method applied it to detect the edges of medical images. The method uses the smoothing theory to smooth the image so as to smooth out some useless details and suppress noise and high-frequency interference components. However, it also smooth out some details of the edge. So the fuzzy en- hancement operator was added to enhance the difference between both sides of the edge, and then the zero-crossing edge detection based on a single scale is adopted to extract the image edge. And finally, this algorithm was compared with the classical sobel and canny operators. The experimental results show that this method solved the contradiction between the accuracy of image edge extraction and the suppression of image noise.