发动机部件CT图像特征提取与区域生长算法

Feature Extraction and Region Growing Algorithm for Processing CT Scans of Engine Parts

针对工业计算机层析成像(CT)图像中金属伪影和噪声会干扰部件分割提取的准确性和精度问题,提出一种基于标准差权重阈值和区域生长的工业CT图像特征提取算法。采用标准差权重的二维最大类间方差和二维最小交叉熵阈值分割方法去除图像背景,利用图像的邻域均值实现多种子点区域自动选取,添加Scharr算子计算梯度改进生长准则完成对部件特征的提取。实验结果表明:该算法相对于其他区域生长法,精确率提高了9.1%,准确率最大接近1,相似性系数提高了5.3%,交并比提高了4.1%最大;该算法部件提取效果更好。

Aiming at the problem that the influence of metal artifacts and noise in industrial computed tomography(CT)images will interfere with the accuracy and precision of part segmentation extraction,a feature extraction method based on standard deviation weight threshold and region growing is proposed for industrial CT images.A two-dimensional maximum between-class variance and two-dimensional minimum cross-entropy threshold segmentation algorithm based on standard deviation weight is proposed to remove the image background.Automatic selection of various sub-point regions is made based on the neighborhood mean of the image.The extraction of component features is completed based on the Scharr operator to calculate the gradient and improve the growth criterion.Experimental results demonstrate that compared with other region growing methods,our algorithm improves accuracy by 9.1%and achieves a maximum pixel accuracy close to 1.The dice score improves by 5.3%while the intersection over union is improves by 4.1%at maximum.Our feature extraction algorithm outperforms other region growing methods.