噪化图像的形态学分割方法

A Mathematical Morphological Approach for Segmenting Heavily Noise-Corrupted Images

对严重噪化的图像 ,提出一个形态学分割方法。先通过灰度骨架变换方法和最大内接块的概念将图像中的颗粒噪声、目标表面及有噪背景分离开 ;移去变化背景及颗粒噪声 ,使图像得以增强 ;为了成功地识别和分离不需要的成分 ,用灰度形态开形成一个尺寸鉴定算法 ;最后在增强图像上应用全局阈值化从背景中获得目标。

In this study ,we propose a method based on mathematical morphology for segmenting images which are heavily corrupted with noise. First we separate the foreground speck noise and the noisy background in the image by means of the gray-scale skeleton transformation and the concept of maximal inscribed blocks. By removing the varying background and speck noise, the image is enhanced. To successfully recognize and separate the unwanted components, a size characterization algorithm is formulated based on the gray-scale morphological opening. Finally, a global thresholding can be applied to the enhanced image to obtain the object from the background. Experimental result is given using artificially generated image.