利用形态学多尺度算法分割粗集料粘连图像

Segmentation of coarse aggregate adhesion images using morphological multiscale algorithm

为准确地对X-ray CT沥青混合料切片图像材质分类过程中存在的颗粒粘连图像进行分割,提出一种利用半径r分别为1、2、3、4的圆形结构元素分别对沥青混合料粗集料粘连图像进行极限腐蚀的改进形态学多尺度算法,通过判断各个分割图像分割线的数目,以分割线数目出现频率最大的分割数作为最终分割,并以最小的结构元素所对应的分割图像作为实际的分割图像,最后通过叠加独立颗粒图像和经粘连分割后的图像生成目标分割图像.最后着重开展了此算法的分割效果和分割精度研究.结果表明:与形态学多尺度算法相比,改进形态学多尺度算法既能有效地分割沥青混合料粗集料粘连图像,又能较好地抑制颗粒的欠分割与过分割现象,并获得较高的分割效果和分割精度,减少数值建模中的难度.

In order to accurately segment the coarse aggregate adhesion images in the CT X-ray asphalt mixture slice image during the classification of materials,an improved morphological multiscale algorithm with structural element radius of 1,2,3 and 4 was introduced to study the segmentation of the coarse aggregate adhesion images,respectively. By judging the number of the segmentation lines,the segmentation image with the maximum number of segmentation lines and minimal structural element was identified as the final segmentation. Then image segmentation was completed by overlapping the independent particle image and the segmented adhesion images. Study of effectiveness and accuracy were carried out to evaluate the improved algorithm. The test results showed that the improved morphological multiscale algorithm not only effectively separated the adhesion images of asphalt mixture Xray CT slices,but also effectively reduced the over-segmentation and less-segmentation problems. The segmentation remains significantly higher in effectiveness and accuracy through this method which will reduce the difficulty of numerical modeling of the sample.