scholarly journals A Study on Weak Edge Detection of COVID-19’s CT Images Based on Histogram Equalization and Improved Canny Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shou-Ming Hou ◽  
Chao-Lan Jia ◽  
Ming-Jie Hou ◽  
Steven L. Fernandes ◽  
Jin-Cheng Guo
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Median Filter ◽  
Histogram Equalization ◽  
Gaussian Filter ◽  
Rapid Spread ◽  
Canny Algorithm ◽  
Edge Detection Method ◽  
Weak Edge ◽  
High Infectivity

The coronavirus disease 2019 (COVID-19) is a substantial threat to people’s lives and health due to its high infectivity and rapid spread. Computed tomography (CT) scan is one of the important auxiliary methods for the clinical diagnosis of COVID-19. However, CT image lesion edge is normally affected by pixels with uneven grayscale and isolated noise, which makes weak edge detection of the COVID-19 lesion more complicated. In order to solve this problem, an edge detection method is proposed, which combines the histogram equalization and the improved Canny algorithm. Specifically, the histogram equalization is applied to enhance image contrast. In the improved Canny algorithm, the median filter, instead of the Gaussian filter, is used to remove the isolated noise points. The K -means algorithm is applied to separate the image background and edge. And the Canny algorithm is improved continuously by combining the mathematical morphology and the maximum between class variance method (OTSU). On selecting four types of lesion images from COVID-CT date set, MSE, MAE, SNR, and the running time are applied to evaluate the performance of the proposed method. The average values of these evaluation indicators are 1.7322, 7.9010, 57.1241, and 5.4887, respectively. Compared with other three methods, these values indicate that the proposed method achieves better result. The experimental results prove that the proposed algorithm can effectively detect the weak edge of the lesion, which is helpful for the diagnosis of COVID-19.

scholarly journals A novel edge detection method based on efficient gaussian binomial filter

2021 ◽  
Vol 7 (2) ◽  
pp. 211
Author(s):  
El Houssain Ait Mansour ◽  
Francois Bretaudeau
Keyword(s):  
Edge Detection ◽  
High Speed ◽  
Detection Method ◽  
Detection Methods ◽  
Gaussian Filter ◽  
Image Edge Detection ◽  
Low Pass ◽  
Image Edge ◽  
Edge Detection Method ◽  
Low Pass Filters

Most basic and recent image edge detection methods are based on exploiting spatial high-frequency to localize efficiency the boundaries and image discontinuities. These approaches are strictly sensitive to noise, and their performance decrease with the increasing noise level. This research suggests a novel and robust approach based on a binomial Gaussian filter for edge detection. We propose a scheme-based Gaussian filter that employs low-pass filters to reduce noise and gradient image differentiation to perform edge recovering. The results presented illustrate that the proposed approach outperforms the basic method for edge detection. The global scheme may be implemented efficiently with high speed using the proposed novel binomial Gaussian filter.

Adaptive Edge Detection Method for Image Polluted Using Canny Operator and Otsu Threshold Selection

2011 ◽  
Vol 301-303 ◽  
pp. 797-804 ◽  
Author(s):  
Jian Guo Yang ◽  
Bei Zhi Li ◽  
Hua Jiang Chen
Keyword(s):  
Edge Detection ◽  
Gaussian Noise ◽  
Impulse Noise ◽  
Detection Method ◽  
Median Filter ◽  
Threshold Selection ◽  
Hybrid Filter ◽  
Canny Operator ◽  
Mean Filter ◽  
Edge Detection Method

In this paper, an adaptive edge detection method (Canny operator and Otsu threshold selection based adaptive edge detection method - COAED) is proposed. The COAED method combines a new hybrid filter with Canny operator to avoid the conflict of Canny operator between noise removing and edge locating, and uses Otsu threshold selection method to determine Dual-threshold of Canny operator adaptively. The new hybrid filter firstly judges whether the pixel is polluted by impulse noise, and then uses a corresponding filter to process the current pixel. A median filter is used if the pixel is thought to be impulse noise; otherwise an improved mean filter is selected to weaken the Gaussian noise. After the image is smoothed by the hybrid filter, a Canny operator with small Gaussian variance is used to extract edge. Because only part of Gaussian noise remains, Canny operator with small Gaussian variance can suppress the noise and preserve the edge effectively. Using the gauge image polluted by hybrid noise as experiment object, the performance of COAED method is evaluated qualitatively and quantitatively. Experimental results show that the COAED method is superior to Canny operator.

scholarly journals Ship infrared image edge detection based on an improved adaptive Canny algorithm

2018 ◽  
Vol 14 (3) ◽  
pp. 155014771876463 ◽  
Author(s):  
Lisang Liu ◽  
Fenqiang Liang ◽  
Jishi Zheng ◽  
Dongwei He ◽  
Jing Huang
Keyword(s):  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Infrared Image ◽  
Histogram Equalization ◽  
Detection Algorithm ◽  
Equalization Algorithm ◽  
Canny Edge ◽  
Image Edge ◽  
Canny Algorithm ◽  
Edge Detection Method

Influenced by light reflection and water fog interference, ship infrared images are mostly blurred and have low signal-to-noise ratio. In this paper, an improved adaptive Canny edge detection algorithm for infrared image of ship is proposed, which aims to solve the threshold of the traditional Canny cannot be adjusted automatically and the shortcomings of sensitivity to noise. The contrast limited adaptive histogram equalization algorithm is adopted to enhance the infrared image, the morphological filter replaces the Gauss filter to smooth the image, and the OTSU algorithm is utilized to adjust the high and low thresholds dynamically. The experimental results show that the improved Canny algorithm, which can not only improve the contrast of the image and automatically adjust the threshold but also reduce the background sea clutter and false edges, is an effective edge detection method.

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