改进的形态学肺部图像边缘检测

针对肺部图像边缘检测中存在的噪声问题,在数学形态学边缘检测的基础上做了3点改进:(1)结合结构元素3个基本选取原则,即形状的相似性、尺寸的覆盖性和不同结构元素的组合性,选取适合肺部图像的全方位结构元和多尺度结构元;(2)改进了普通的形态学边缘检测算子,将全方位结构元和多尺度结构元相结合,得到适用于肺部图像的新型复合形态学边缘检测算子;(3)将峰值信噪比(Peak signal-to-noise ratio, PSNR)加入权值计算方法中,改进了权值的计算方法。最后通过仿真实验,对PSNR为50684 9 dB的肺部噪声图像进行边缘检测,并与一般算法进行比较,结果表明改进算法在PSNR和均方误差(Mean square error, MSE)上均有明显改善,能够检测出更清晰、去噪效果更好的肺部图像边缘。应用于其他图像或加入不同噪声时,本文算法也能检测出更清晰的图像边缘,表明该算法具有很好的鲁棒性。

Edge Detection of Lung Images Based on Improved Morphology

In view of the noise problems when detecting the edge of lung images, three points are improved based on the mathematical morphology edge detection. Firstly, connected with three fundamental select principles for the structuring element, i.e. the similarity of shape, the covering of size and the composition of the different structuring elements, it particularly chooses omnidirectional structures and multi-scale structures suitable for the lung images. Secondly, it improves common morphological edge detection operator, and combines omnidirectional structures and multi-scale structures to obtain a new compound morphology edge detection operator which is suitable for edge detection of the lung images. Thirdly, it adds peak signal-to-noise ratio (PSNR) into weight calculation method and improves the method for calculating weights. Finally, it detects the edge of lung noise images with PSNR of 50.684 9 dB through the simulation. Compared with the general algorithm, The results show that the improved algorithm can improve PSNR and mean square error (MSE) substantially and can detect more and better de-noising lung image edge. Applied to other images or different noise images, the proposed algorithm can detect sharper edges of the image, indicating that the algorithm has good robustness.