Traditional Canny Edge Detection Algorithm Research Articles

Applied Improved Canny Edge Detection for Diagnosis Medical Images of Human Brain Tumors

Medical image processing has become one of the crucial elements of the diagnostic process because of the increased usage of medical imaging recently, and clinicians' dependence on such computer-processed medical images in diagnosing patients. As the traditional Canny edge detection algorithm is sensitive to noise, it is easy to lose weak edge information when filtering out the noise, and its fixed parameters show poor adaptability. The suggested algorithm introduced the concept of image block intensity operator to replace image gradient. In addition, the computing speed of the suggested algorithm is relatively fast because it works block by block rather than pixel by pixel. Two adaptive threshold selection methods are presented, one based on the median accumulative histogram of image gradient magnitude and the other on the standard deviation for both types of image pixels (one with less edge information and the other with rich edge information). The proposed algorithm can be dividing into four stages: Input the medical digital image, convert the color medical image to gray-scale, applied improved canny edge detection, then calculate the MSE & PSNR Measures, in addition conduct a visual questionnaire by oncologists to find out which method that made the enhancement of the medical image clearer.

Edge Detection Algorithm-Based Lung Ultrasound in Evaluation of Efficacy of High-Flow Oxygen Therapy on Critical Lung Injury.

The study focused on the therapeutic effects of high-flow oxygen therapy on patients with critical lung injury using edge detection-based ultrasound images. Firstly, the traditional Canny edge detection algorithm was improved, and the optimal threshold was obtained by optimizing the median filter and combining Otsu algorithm and threshold iteration method. Then, the optimized algorithm was compared with the traditional Canny edge detection algorithm and applied to process the lung ultrasound images of 120 cases of critical lung injury, to compare the efficacy of high-flow oxygen therapy and the traditional oxygen therapy. It was found that the peak signal-to-noise ratio (PSNR) (20.34~31.3), edge intensity value (17.89~27.34), and edge detection effect of the improved Canny algorithm were better than the traditional Canny algorithm (15.2~28.61, 9.44~18.56). The failure rate of extubation (4.1%), reintubation rate (0.8%), comfort (2.38 ± 0.15 points), dry humidity score (1.07 ± 0.21 points), antibiotic use (7.41 ± 0.74 days), and hospital stay (8.66 ± 1.02 days) in the experimental group were significantly lower than the corresponding indexes in the control group (11.7%, 5%, 4.25 ± 0.26 minutes, 4.94 ± 0.78 minutes, 19.29 ± 1.7 days, and 27.49 ± 2.22 days), and the difference was statistically significant (P < 0.05). In the experimental group, within 48 hours after extubation, the respiratory rate (RR), heart rate (HR), arterial partial pressure of carbon dioxide (PaCO2), and HCO3− were significantly lower than those of the control group; and the values of transcutaneous oxygen saturation (SpO2), mean arterial pressure (MAP), arterial partial pressure of oxygen (PaO2), and pH were significantly higher than the control group, and the difference was statistically significant (P < 0.05). In conclusion, the algorithm in this study is superior to the traditional Canny algorithm, and the high-flow oxygen therapy can reduce the failure rate of extubation, strengthen patient comfort, improve the degree of gas humidification, stabilize the respiratory function and circulatory system, and shorten the time of antibiotic use and hospital stay.

A Low-Complexity and Adaptive Extraction Method for Reflection Hyperbolic Edges in Impulse GPR Images

Traditional Canny edge detection algorithm has been widely used for the extraction of hyperbolic edges in ground penetrating radar (GPR). However, the Canny edge detection algorithm cannot adaptively determine the segmentation thresholds. Moreover, multiple hyperbolic edges are usually detected for each target, which increases the complexity of subsequent processing. In this letter, we propose a novel method that obtains only one hyperbolic edge for each target and automatically determines the segmentation threshold. Both simulation and real data results show that the proposed method is able to extract the target hyperbolic edges precisely and significantly improves the efficiency of target detection combined with the Hough transform in impulse GPR systems.

Research on Automatic Color Scale Optimization Based Canny Identification Strategy for Underwater Fish

In order to solve the problem of the traditional Canny edge detection algorithm, due to the inaccurate selection of the threshold value, the recognition rate is low in the fish edge detection process. This paper proposes an adaptive Canny edge detection algorithm. The image enhancement is carried out by using automatic color scale, and the optimal recognition of underwater fish target is completed. The experimental results show that the improved algorithm and edge contour are more complete, which proves the effectiveness of the fish density recognition method.

Edge Detection of Agricultural Products Based on Morphologically Improved Canny Algorithm

The traditional canny edge detection algorithm has its limitations in the aspect of antinoise interference, and it is susceptible to factors such as light. To solve these defects, the Canny algorithm based on morphological improvement was proposed and applied to the detection of agricultural products. First, the algorithm uses the open and close operation of morphology to form a morphological filter instead of the Gaussian filter, which can remove the image noise and strengthen the protection of image edge. Second, the traditional Canny operator is improved to increase the horizontal and vertical templates to 45° and 135° to improve the edge positioning of the image. Finally, the adaptive threshold segmentation method is used for rough segmentation, and on this basis, double detection thresholds are used for further segmentation to obtain the final edge points. The experimental results show that compared with the traditional algorithm applied to the edge detection of agricultural products, this algorithm can effectively avoid the false contour caused by illumination and other factors and effectively improve the antinoise interference while more accurate and fine detection of the edge of real agricultural products.

Improvement of the Traditional Canny Edge Detection Algorithm by using Combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter

Canny Edge Detection Algorithm was very popular on the computer vision area which used to preserve the edges of the image. Due to the defect of the Canny Edge Detection Algorithm like no efficiency on noise removal, some improvement on the Canny Edge Detection Algorithm was done by the researchers. On this paper, a new enhanced Canny Edge Detection Algorithm will be propose which replaces the Gaussian Filter with combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter. The replace of Gaussian Filter with combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter is to improve the performance of Canny Edge Detection Algorithm on noise removal. A comparison between Canny Edge Detection Algorithm proposed by this paper, Canny Edge Detection Algorithm proposed by (Ilkin, Tafralı, &amp;Sahin, 2017) and traditional Canny Edge Detection Algorithm will be done. The comparison will done by using eight images with different type and size which corrupted by noise. The performance of three algorithms will be determined by using the Peak Signal to Noise Ratio (henceforth, PSNR) value which uses as a quantitative measure. From the result, the Canny Edge Detection proposed by this paper will provide a better performance on noise removal and which will give a better impact on preserve the edges of the images corrupted by noise.

Improved Canny Edge Detection Technique Using S-Membership Function

Traditional Canny edge detection algorithm is sensitive to noise, hence it may lose the weak edge information after noise removal and show poor adaptability of fixed parameters like threshold values. In view of these problems, this paper reports on the modification of canny edge detection algorithm using s-membership function. Adaptability of threshold values are achieved through S-membership function and is given as input to default Canny algorithm. The grayscale images have been analyzed for default Canny and modified Canny algorithm. To understand the performance of these algorithms it is essential to evaluate various statistical metrics. The proposed work states that the detailed statistical results and the images obtained reveal the superior performance of the modified Canny algorithm over the default Canny edge detection algorithm. Further the images obtained from modified Canny algorithm shows the marked edges with efficient image edge extraction and provide accurate information for image measurement.

Foreign object recognition technology for port transportation channel based on automatic image recognition

In the port transportation, the transportation vehicle is extremely easy to cause a bad condition in the transportation process due to the influence of foreign matter, and the current foreign matter detection is still carried out by manual methods. Based on this, this study is based on the automatic image recognition technology, using the binocular imaging system for video collection and analyzing the video images for foreign object recognition. We use extreme median filtering to perform image noise processing and improve the traditional Canny edge detection algorithm to obtain an improved edge detection method for Canny operator. The algorithm is used to perform image edge detection, and the image gray histogram is used to enhance the foreign object image processing. Combined with the experimental analysis, the detection method of this study has certain effects, which can be used for foreign object identification in port transportation channels and can provide theoretical reference for subsequent related research.

Polynomial Interpolation Sub-Pixel Edge Detection Method Based on Improved Canny Operator

According to the shortcoming of traditional Canny edge detection algorithm is sensitive to noise and low positioning accuracy, this paper proposes an algorithm of Polynomial interpolation Sub-pixel edge detection based on improved Canny operator: We first use improved Canny operator edge detection algorithm to extract rough image edge, then use the quadratic Polynomial interpolation to calculate on the rough extraction edge, finally refine the edge image. Experiments show that the improved method is better than the traditional detection method can accurately locate the image edge.

An Improved Edge Detection Algorithm Based on Canny Operator

The traditional Canny edge detection algorithm is analyzed in this paper. To overcome the difficulty of threshold selecting in Canny algorithm, an improved method based on Otsu algorithm and mathematical morphology is proposed to choose the threshold adaptively and simultaneously. This method applies the improved Canny operator and the image morphology separately to image edge detection, and then performs image fusion of the two results using the wavelet fusion technology to obtain the final edge-image. Simulation results show that the proposed algorithm has better anti-noise ability and effectively enhances the accuracy of image edge detection.

Enhanced Edge Detection Technique for High-Resolution Satellite Imagery Using a Curvelet-Canny Approach

In this research paper, a new approach using the capability of the second generation curvelet transform together with the traditional canny operator for edge detection of highresolution satellite imagery; the combined technique will be applied on WorldView-2 imagery. First, the curvelet coefficients will be generated in multi-scales and multi-directions using a forward discrete curvelet algorithm. Then, these coefficients will be sorted in each scale to generate the edge map using the larger coefficients for the coarser scales. Second, this edge map will be the input to the second stage where the three main steps of the traditional canny operator, gradient calculation, non-maximal suppression and hysteresis, will be applied. The first step results in removing noise, fine edges, from the image aiding the second step for better connecting the strong edges without the effect of weak edges coming from the noise. The percentage of the utilised coefficients in the curvelet transforms step together with the weight for each scale are the tuning parameters the user has to adjust to get the desired level of edges detected. The results from the proposed approach were compared to the traditional canny edge detection algorithm. The results showed very good potentials for detecting elongated edges and also for generating more closed objects, which make this method a good alternative for the segmentation step for any further object-based classification algorithm.

An Improved Canny Edge Detection Algorithm

Traditional Canny edge detection algorithm uses a global threshold selection method, when large changes are in the background of the image and the target gray, global threshold method may lose some local edge information. For this problem, this paper therefore proposes an adaptive dynamic threshold improved Canny edge detection algorithm. The method uses image gradient variance as the criterion of the image block according to the four forks tree principle, then uses the Otsu method to get the corresponding sub-block threshold value for each sub-block, and obtains threshold value matrix by interpolation, finally, gets image edge with improved edge connected algorithm. Experimental results show that, the algorithm not only has good anti-noise performance, but also better detection accuracy.

Research of Image Pre-processing Algorithm Based on FPGA

Abstract How to design a low-cost , reliable and real-time target recognition system with large amount of data has become a hot topic in the area of image processing .However, Edge detection has played an important role in target recognition system. The threshold of traditional canny edge detection algorithm must be setting by human, and has a large number of calculations. In order to overcome the shortcomings of the traditional Canny algorithm, proposing an adaptive threshold edge detection algorithm, and realizing it by hardware. This paper will introduce the implementation of the common low-level image processing algorithm in the FPGA, including color space convert module , edge extraction algorithms module , Hough transform module .The results of the experiment indicate that to realize the large amount of calculation of image processing by FPGA hardware logic, not only improves the effect of image processing, but also has high real-time!

A Study and Improvements on Canny Algorithm

This paper studies principle of the traditional Canny edge detection algorithm and the existing problems. Some solutions toward the problems are also proposed. The improvement methods consisted of two parts. On the one hand, as Canny edge algorithm itself does not have strong noise immunity while ensuring edge positioning accuracy, an adaptive median filter denoising program based on the Canny Gaussian filter is proposed to enhance the Canny algorithm filter performance. On the other hand, given the Canny algorithm high and low threshold parameter is not determined by the feature information of the image edge, but rather set manually which does not have the adaptive ability, this paper proposes an adaptive threshold setting scheme for high and low thresholds to improve the adaptive ability of Canny algorithm to different images.

Algorithm for the Detection of Feeble Edge of Complex Image

Image weak edge detection has a wide application. An improved ant colony algorithm is used for image edge detection . When image background is very complex ,the weak edge detection is more difficult. Around the key steps in the existing ant colony algorithm , we propose two improved methods. First, the expression of pheromone has been improved, and second, to improve the heuristic information value is calculated. Finally, the improved ant colony algorithm with the traditional Canny edge detection algorithm are compared.The improved edge detection experiments show that the new algorithm is position detection accurate, continuity, and the advantages of less interference.