发动机部件CT图像特征提取与区域生长算法

doi:10.12382/bgxb.2021.0893

摘要/Abstract

摘要：

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。

关键词: 特征提取, 发动机CT图像, 二维OTSU法, 二维最小交叉熵, 标准差权重

Abstract:

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.

Key words: feature extraction, engine CT image, two-dimensional OUST method, two-dimensional minimum cross entropy, standard deviation weight

章斌, 卢洪义, 刘舜, 桑豆豆, 杨禹成. 发动机部件CT图像特征提取与区域生长算法[J]. 兵工学报, 2023, 44(4): 1171-1180.

ZHANG Bin, LU Hongyi, LIU Shun, SANG Doudou, YANG Yucheng. Feature Extraction and Region Growing Algorithm for Processing CT Scans of Engine Parts[J]. Acta Armamentarii, 2023, 44(4): 1171-1180.

图/表 13

图1 标准差阈值和改进区域生长算法的流程图

Fig.1 Standard deviation threshold and flowchart of the improved region growing algorithm

表1 发动机调节器和涡轮CT切片非局部均值滤波处理结果

Table 1 Results of non-local mean filtering processing of engine conditioner and turbo CT slices

发动机部件	CT切片原始图像	滤波后图像
调节器
发动机涡轮

图2 灰度-领域灰度均值二维直方图

Fig.2 2D grayscale mean histogram of the grayscale-domain

图3 改进阈值分割

Fig.3 Improved threshold segmentation

图4 区域生长法流程图

Fig.4 Flowchart of the regional growing method

图5 多种子自动选取流程图

Fig.5 Flowchart of the automatic selection process of seed points

图6 数字图像邻域

Fig.6 Digital image neighborhood

图7 改进区域生长法

Fig.7 Improved regional growing method

图8 发动机CT原始图像

Fig.8 Raw CT images of the engine

表2 本文算法提取结果

Table 2 Extraction results of the improved algorithm

发动机部件	调节器	发动机涡轮
CT图像预先分割图
灰度值直方图
特征提取结果

表3 4种算法结果对比

Table 3 Comparison of four algorithms

发动机部件	传统区域生长法	最大熵法	文献[14]算法	本文算法
调节器
发动机涡轮

图9 设计图纸提取理想结果

Fig.9 Design drawings for extracting ideal results

表4 不同算法对比评价指标值

Table 4 Comparison of evaluation index values of different algorithms

算法	发动机部件	C_precision	P_accuracy	D_dice	I_intersection
传统区域生长法	调节器	0.955	0.998	0.957	0.918
	发动机涡轮	0.824	0.995	0.895	0.810
最大熵法	调节器	0.731	0.993	0.845	0.731
	发动机涡轮	0.920	0.998	0.958	0.920
文献[14]算法	调节器	0.899	0.997	0.942	0.890
	发动机涡轮	0.971	0.997	0.935	0.878
本文算法	调节器	0.981	0.998	0.962	0.927
	发动机涡轮	0.996	0.999	0.985	0.970

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