Edge detection is an effective method for image segmentation and feature extraction. Therefore, extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019 (COVID19) CT images is extremely important. Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy. In this paper, we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex (GF_SSR), and improved multiscale morphology and adaptive threshold binarization (IMSM_ATB). As all the CT images have noise, we propose to remove image noise by Gaussian filtering. The edge of CT images is enhanced using the SSR algorithm. In addition, based on the extracted edge of CT images using improved Multiscale morphology, a particle swarm optimization (PSO) algorithm is introduced to binarize the image by automatically getting the optimal threshold. To evaluate our method, we use images from three datasets, namely COVID19, KaggleCOVID19, and COVIDChestxray, respectively. The average values of results are worthy of reference, with the Shannon information entropy of 1.8539, the Precision of 0.9992, the Recall of 0.8224, the FScore of 1.9158, running time of 11.3000. Finally, three types of lesion images in the COVID19 dataset are selected to evaluate the visual effects of the proposed algorithm. Compared with the other four algorithms, the proposed algorithm effectively detects the weak edge of the lesion and provides help for image segmentation and feature extraction.
COVID19 is a disease in the worldwide spread, severely threatening the lives of people around the world. Computer tomography (CT) is an important means of clinical diagnosis of COVID19, which can help doctors diagnose patients’ respiratory symptoms, especially uncertain SARSCoV2 (severe acute respiratory syndrome coronavirus infection with negative PCR (Polymerase Chain Reaction)) [
In recent years, in order to detect the continuous and complete edge, many scholars have put forward a lot of effective methods [
Motivated by previous work, we are proposing an edge detection method that employs Gaussian filtering, SSR, improved multiscale morphology, and threshold binarization. Specifically, our method removes image noise and enhances image detail by Gaussian filtering and SSR algorithm. In addition, the improved multiscale morphology and threshold binarization, which are used to accurately extract the weak edge of COVID19 CT images. Moreover, we test the performance of the proposed method by selecting weak edges and intensity inhomogeneities medical images in the COVID19 dataset. Meanwhile, we compare our method’s performance with other methods. We believe that the techniques used in the proposed method can provide more meaningful information for feature extraction and lesion segmentation, since the edge detection accuracy has improved.
The main contribution to this paper is presented below:
In the context of Gaussian filtering, SSR, improved multiscale morphology, and the optimization of threshold based on PSO, we propose a weak edge detection algorithm.
Adding a multiscale filter to the multiscale morphology to remove image noise.
In order to make the detected edge clearer, our method considers using the PSO algorithm to binarize the image by automatically getting double thresholds.
The rest of this paper is organized as follows.
In this section, firstly, a quick review of Gaussian filtering and SSR is followed by a brief description of the improved multiscale morphology. Then, the optimization of threshold based on PSO. Meanwhile, in
In this work, the GF_SSR algorithm is used for image filter and enhancement. In general, Gaussian filtering can remove Gaussian noise. Although SSR cannot remove hidden noise, it possesses the ability to enhance the image contrast.
Gaussian filtering is a linear smoothing filter based on the Gaussian function which is widely used to remove Gaussian noise [
The retinex theory is a widely used image enhancement algorithm based on human visual perception, which is determined by the color of the image, not by light and other factors [
Based on the above principle, many scholars have proposed various image enhancement algorithms, including SSR and multiscale retinex (MSR) [
The steps for edge detection by improved multiscale morphology are as follows: (1) Taking structural element
Mathematical morphology has been widely applied for edge detection. Multiscale morphology can be used to detect the edge of different directions, which basic elements including original data and structural elements [
Expansion, erosion, opening, and closing operations are the most basic operations in multiscale morphology, and opening and closing operations are defined by erosion and expansion [
The initialized structure element
In addition to filtering, morphological processing can also obtain the edge. Assume that the image boundary is
In order to reduce the influence of noise, the nonaverage weight is used as the weighting coefficient for edge detection at different scales. The calculation formula is as
We select multiscale edge fusion algorithm for obtaining the edge image
Obtaining the results indicate that, by the above steps, the detected edges are relatively thick and many nonedges are detected as edges. Then, we add the following algorithm to get a more accurate boundary.
PSO algorithm is an evolutionary computation technology from seeking the optimal solution of the path by studying the cooperation and information sharing between individuals of the birds’ predation [
The edge detection results are evaluated using Shannon Information Entropy (SIE), Precision, Recall, FScore, and the average time [
Also, Precision, Recall, FScore are considered to be as quantitative evaluation index. Moreover, Precision represents how many of the samples predicted to be positive are correct. Recall represents the probability that the prediction is positive in the true value. FScore considers the harmonic value of Precision and Recall. Precision, Recall, and FScore are defined as
The proposed edge detection algorithm mainly consists of two parts: (1) Using Gaussian filtering and SSR to preprocess the CT image which can remove image noise and enhance the image detail. (2) Extracting the image edge by the improved multiscale morphology and PSO algorithm. The principle of the algorithm is shown in
In this section, we firstly introduce the datasets. Then, to demonstrate the effectiveness of our method, we compare several edge detection models in three datasets, including multiscale morphological, the MMTS algorithm, the GFMM algorithm, and the MTHT algorithm. Further, comparing results of these algorithms is given.
In this section, we discussed the dataset used. The images used in this study were acquired from three datasets to evaluate the performance of the proposed method.
The comparison results using COVID19 are summarized in
Method  SIE  Precision  Recall  FScore 

Multiscale morphology  5.5519  0.9977  0.9202  1.8233 
MMTS  4.6986  0.9971  0.9818  1.9103 
GFMM  2.5209  0.9957  0.8857  1.8233 
MTHT  5.5519  0.9993  0.4705  0.9318 
The proposed method 
Method  SIE  Precision  Recall  FScore 

Multiscale morphology  4.5318  0.9979  0.0233  0.0461 
MMTS  5.0070  0.9822  0.9316  1.8533 
GFMM  5.0761  0.9822  0.9361  1.8533 
MTHT  2.1269  0.9996  0.7298  1.4451 
The proposed method  

Method  SIE  Precision  Recall  FScore 

Multiscale morphology  5.5317  0.9979  0.0233  0.0461 
MMTS  5.4781  0.9769  0.9992  1.6904 
GFMM  5.5317  0.9866  0.7298  1.8432 
MTHT  2.6603  0.9944  0.4107  0.8136 
The proposed method  

In addition, we need to calculate the average time of the CT images from COVID19, COVIDChestxray, and KaggleCOVID19, meanwhile, the average time is shown in
Considering all factors, we believe that the experimental results of the proposed algorithm on COVID19 are better than KaggleCOVID19 and COVIDChestxray.
The subjective evaluation indicator of edge detection is determined by the good visibility of the detected edge. Using the proposed model, the edge detection results of three type’s lesions from the COVID19 dataset are shown in
To verify the effect of edge detection, the multiscale morphology algorithm, the MMTS algorithm, the GFMM algorithm, and the MTHT algorithm, and the proposed algorithm are used to carry out experiments on paving road shape. The result obtained is shown in
In our experiment, we test the effect of edge detection in single ground glass shadow, the result is shown in
As an assistant feature extraction and segmentation means, edge detection is expected to offer more accurate information. In multiple ground glass shadow images, the subjective performance comparisons among five algorithms are shown in
This paper studies the capability of edge detection at CT images of COVID19. For detecting CT images edge, a method has been proposed, combining GF_SSR and IMSM_ATB. Based on the Gaussian filter and SSR algorithm, we can remove noise and enhance the weak edge. In addition, we use improved Multiscale morphology to detect the image's initial edge, however, which is not clear. Finally, we have proposed the use of the PSO algorithm to obtain dual thresholds for image binarization, thereby significantly enhancing the weak edge and producing the final edge image. In the experiment, five closely related algorithms namely multiscale morphology, MMTS, GFMM, MTHT, and the proposed method are compared to check the denoising and image edge detection effect. Meanwhile, we select Shannon Information Entropy, Precision, Recall, FScore, and the running time is as evaluation indexes. In this work, according to the test results of Shannon information entropy, Precision, Recall, FScore, and the running time, we find that the proposed method is better than multiscale morphology, MMTS, GFMM, and MTHT. Moreover, compared with the KaggleCOVID19 and the COVIDChestxray datasets, the COVID19 dataset can get better results. The simulation results indicate that the proposed method can improve the edge detection accuracy and robustness to noise than the other methods on the COVID19 dataset. Therefore, the high efficiency and the relatively high precision can be considered as one of the advantages of the proposed method at the edge detection. In the future, we should further study the connectivity of weak edges in order to obtain better detection results.