Method for obtaining noise of thoracic CT images based on Canny edge detection algorithm
10.3760/cma.j.cn121382-20240307-00301
- VernacularTitle:基于Canny边缘检测算法求取CT胸腔图像噪声的方法
- Author:
Ying LIU
1
;
Minghao SUN
;
Jingying SHEN
Author Information
1. 上海理工大学健康科学与工程学院，上海 200093
- Keywords:
Hough transform;
Thoracic CT images;
Edge detection algorithm;
Average noise;
Noise standard deviation;
Noise level
- From:
International Journal of Biomedical Engineering
2024;47(3):205-212
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To propose a method for obtaining noise from thoracic CT images based on the Canny edge detection algorithm.Methods:A total of 250 pieces of thoracic CT images of male volunteers collected in 2021 were selected. The contours of thoracic CT images were extracted by the Canny adaptive threshold method. The similarity of the Sobel algorithm, the Canny double threshold method, and the Canny adaptive threshold method was compared. The Hoff transform was used to determine the regions of interest. The effects of the selection size of the regions of interest, the reconstructed convolution kernel, and tube current on the noise of thoracic CT images were investigated.Results:The Canny adaptive threshold method preserved more detail, and the continuity and integrity of the edges were improved, indicating that it was more flexible and robust for edge detection and image segmentation. The Canny adaptive threshold method had the highest structural similarity index (0.644) and the lowest root mean square error (0.371). It had the highest similarity in edge contour detection, and the effect was more significant. As the size of the square area of interest increased, the average noise decreased. The noise standard deviation increased in some intervals, especially in larger square regions. In the case of the same reconstructed convolution kernel, the mean noise of the ascending aorta in thoracic CT images was higher than that of the thoracic aorta. The noise standard deviation of the ascending aorta was lower than that of the thoracic aorta. For ascending aorta, the mean ascending aorta noise (41.97 dB) of reconstructed convolutional nucleus E was the lowest, with the highest noise standard deviation (20.64 dB). For the thoracic aorta, the mean noise (30.78 dB) of the reconstructed convolutional nucleus E was the lowest. The mean noise and standard deviation of the thoracic aorta decreased with the increase in tube current.Conclusions:A method based on the Canny edge detection algorithm to obtain noise from thoracic CT images is proposed, which is suitable for detecting thoracic CT images.
