Computed tomography reconstruction based on canny edge detection algorithm for acute expansion of epidural hematoma

Chen, Dan; Cheng, J.; He, Hao-Yuan; Ma, Chao; Yao, Liang; Jin, Chuan-Bing; Cao, Yanghui; Li, Jia; Ji, Peng

doi:10.1016/j.jrras.2022.07.011

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…In addition, in order to analyze and view the results more conveniently and intuitively, this study normalizes all the results, as shown in Figure 5 . The non-subsampled contourlet transform (NSCT), Tenengrad algorithm [ 29 ], Roberts algorithm [ 30 , 31 ], discrete cosine transform (DCT) [ 32 , 33 ], energy of gradient (EOG) [ 34 ] algorithm, Canny algorithm [ 35 , 36 ], and Laplacian algorithm [ 37 , 38 ] are selected for this comparative experiment. The Tenengrad algorithm uses a Sobel [ 39 , 40 ] operator to extract gradient values in horizontal and vertical directions, and then calculates the gradient square sum of all pixels.…”

Section: Resultsmentioning

confidence: 99%

A Method for Medical Microscopic Images’ Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains

Zhou

et al. 2022

Sensors

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An algorithm for a sharpness evaluation of microscopic images based on non-subsampled shearlet wave transform (NSST) and variance is proposed in the present study for the purpose of improving the noise immunity and accuracy of a microscope’s image autofocus. First, images are decomposed with the NSST algorithm; then, the decomposed sub-band images are subjected to variance to obtain the energy of the sub-band coefficients; and finally, the evaluation value is obtained from the ratio of the energy of the high- and low-frequency sub-band coefficients. The experimental results show that the proposed algorithm delivers better noise immunity performance than other methods reviewed by this study while maintaining high sensitivity.

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Section: Resultsmentioning

confidence: 99%

A Method for Medical Microscopic Images’ Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains

Zhou

et al. 2022

Sensors

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“…The results show that the Dice value for hematoma segmentation on the ground of the Canny algorithm is 0.919. The Canny EDA has good accuracy in the segmentation of epidural hematoma [15]. F. Jiang's team introduced an Otsu method combined with the Canny operator to reduce the harm of tunnel construction accidents to personnel.…”

Section: Related Workmentioning

confidence: 99%

Traffic Marking Extraction Algorithm Based on Image and Point Cloud Data

Wu,

Mao

2024

IEEE Access

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With the continuous development of urban traffic and the improvement of intelligent transportation systems, the accurate extraction and analysis of road traffic markings becomes more and more important. At present, road traffic marking is extracted by collecting dense point cloud data with highthread LiDAR. However, due to the high cost of the high-thread LiDAR and the large amount of point cloud data, the production efficiency of high-precision maps is low, and updating is complex and difficult. Therefore, a traffic marking extraction algorithm based on image and point cloud data fusion is proposed. The innovation of this study lies in the introduction of image processing for traffic marking edge detection, and the adaptive threshold segmentation of point cloud data using the maximum inter class variance method. The algorithm first uses an edge detection algorithm on the ground of the Canny operator to extract edge information of traffic markings from road images. Meanwhile, it uses the maximum inter class variance method to extract the internal point coordinate information of traffic markings from point cloud data. Subsequently, clustering segmentation, geometric correction and other techniques are used to process the fused data, thereby achieving automated extraction of road traffic markings. The results show that on the TuSimple dataset, the false detection rate and missed detection rate of the edge detection algorithm on the ground of the Canny operator are only 3.84% and 2.79%, respectively. Meanwhile, in the TuSimple dataset, the peak signal-to-noise ratio and structural similarity of the maximum inter class variance method were as high as 39.25% and 9.23%, respectively. In addition, the average time it takes for the algorithm proposed by the research institute to extract the edge points of a single road traffic marking is only 5.69ms. Especially, the proposed method achieved a maximum improvement of 14.07% in the accuracy of traffic marking edge detection. The method proposed by the research institute effectively reduces false positives and missed detections, and can accurately and efficiently extract the edges of road traffic markings, providing a new and reliable solution for the automated extraction of road traffic markings.

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“…There are some defects in the algorithms commonly used in edge detection, for example, the canny algorithm [ 29 , 30 , 31 , 32 , 33 ] uses Gaussian filtering for smoothing and noise reduction, which only considers the similarity of images in the spatial domain, and the filtering process leads to the loss of some useful weak edges. At the same time, the selection of the size of the convolution kernel of Gaussian filtering is influenced by human factors and, if the value is too small, the noise of the image cannot be effectively suppressed and the smoothed image is blurred.…”

Section: Related Workmentioning

confidence: 99%

“…Deep learning method: as an important branch of machine vision, deep learning technology has been undergoing rapid development in recent years and has been used more and more widely in the industrial field. Yolo [ 29 , 30 , 31 , 32 ] is a type of neural network which is used to detect the target online. It transforms the object detection into a regression problem.…”

Section: Related Workmentioning

confidence: 99%

The Detection of Yarn Roll’s Margin in Complex Background

Wang

Shi

et al. 2023

Sensors

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Online detection of yarn roll’s margin is one of the key issues in textile automation, which is related to the speed and scheduling of bobbin (empty yarn roll) replacement. The actual industrial site is characterized by uneven lighting, restricted shooting angles, diverse yarn colors and cylinder yarn types, and complex backgrounds. Due to the above characteristics, the neural network detection error is large, and the contour detection extraction edge accuracy is low. In this paper, an improved neural network algorithm is proposed, and the improved Yolo algorithm and the contour detection algorithm are integrated. First, the image is entered in the Yolo model to detect each yarn roll and its dimensions; second, the contour and dimensions of each yarn roll are accurately detected based on Yolo; third, the diameter of the yarn rolls detected by Yolo and the contour detection algorithm are fused, and then the length of the yarn rolls and the edges of the yarn rolls are calculated as measurements; finally, in order to completely eliminate the error detection, the yarn consumption speed is used to estimate the residual yarn volume and the measured and estimated values are fused using a Kalman filter. This method overcomes the effects of complex backgrounds and illumination while being applicable to different types of yarn rolls. It is experimentally verified that the average measurement error of the cylinder yarn diameter is less than 8.6 mm, and the measurement error of the cylinder yarn length does not exceed 3 cm.

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Computed tomography reconstruction based on canny edge detection algorithm for acute expansion of epidural hematoma

Cited by 8 publications

References 27 publications

A Method for Medical Microscopic Images’ Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains

A Method for Medical Microscopic Images’ Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains

Traffic Marking Extraction Algorithm Based on Image and Point Cloud Data

The Detection of Yarn Roll’s Margin in Complex Background

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