Research on edge detection algorithm based on improved sobel operator

Han, Ling; Yimin, Tian; Qianhui, Qi

doi:10.1051/matecconf/202030903031

Cited by 33 publications

(17 citation statements)

References 7 publications

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“…As the segmented image in Section 2.4.6 , we perform some experiments to compare with various edge detectors such as the Sobel edge operator [ 37 ], Robert edge operator [ 38 ], Prewitt edge operator [ 39 ], and Canny edge operator [ 39 ], for the segmentation of the boundaries of breast cancer on Figure 9 c. Figure 10 shows the result in this case. According to Figure 10 a, the Sobel edge detector will be more accurate in detecting the boundaries of breast cancer and more flexible to unseen image datasets.…”

Section: Methodsmentioning

confidence: 99%

“…As the segmented image in Section 2.4.6, we perform some experiments to compare with various edge detectors such as the Sobel edge operator [37], Robert edge operator [38], Prewitt edge operator [39], and Canny edge operator [39], for the segmentation of the Segmenting and removing small pixels by morphological modeling based on a dilation operator, (a) breast cancer segmented using KM++CSO and OTSU's threshold method, (b) image reconstructed with structure element of 7 to disconnect component pixels of the binary image, (c) regional maxima image preserved and all small objects removed using global thresholding. This is an iterative algorithm for automatic estimation of threshold T given as follows:…”

Section: Edge Detectionmentioning

confidence: 99%

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Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization

Wisaeng

2022

Diagnostics

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Traditional breast cancer detection algorithms require manual extraction of features from mammogram images and professional medical knowledge. Still, the quality of mammogram images hampers this and extracting high–quality features, which can result in very long processing times. Therefore, this paper proposes a new K–means++ clustering based on Cuckoo Search Optimization (KM++CSO) for breast cancer detection. The pre-processing method is used to improve the proposed KM++CSO method more segmentation efficiently. Furthermore, the interpretability is further enhanced using mathematical morphology and OTSU’s threshold. To this end, we tested the effectiveness of the KM++CSO methods on the mammogram image analysis society of the Mini–Mammographic Image Analysis Society (Mini–MIAS), the Digital Database for Screening Mammography (DDSM), and the Breast Cancer Digital Repository (BCDR) dataset through cross-validation. We maximize the accuracy and Jaccard index score, which is a measure that indicates the similarity between detected cancer and their corresponding reference cancer regions. The experimental results showed that the detection method obtained an accuracy of 96.42% (Mini–MIAS), 95.49% (DDSM), and 96.92% (BCDR). On overage, the KM++CSO method obtained 96.27% accuracy for three publicly available datasets. In addition, the detection results provided the 91.05% Jaccard index score.

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Section: Methodsmentioning

confidence: 99%

“…As the segmented image in Section 2.4.6, we perform some experiments to compare with various edge detectors such as the Sobel edge operator [37], Robert edge operator [38], Prewitt edge operator [39], and Canny edge operator [39], for the segmentation of the Segmenting and removing small pixels by morphological modeling based on a dilation operator, (a) breast cancer segmented using KM++CSO and OTSU's threshold method, (b) image reconstructed with structure element of 7 to disconnect component pixels of the binary image, (c) regional maxima image preserved and all small objects removed using global thresholding. This is an iterative algorithm for automatic estimation of threshold T given as follows:…”

Section: Edge Detectionmentioning

confidence: 99%

Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization

Wisaeng

2022

Diagnostics

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“…Sobel operator is a common edge detection operator [ 26 , 27 , 28 ]. Sobel operator calculates the gradient of the corresponding region of the image.…”

Section: Stokes Vector and Polarized Imagesmentioning

confidence: 99%

Defogging Algorithm Based on Polarization Characteristics and Atmospheric Transmission Model

Zhang

Shi

et al. 2022

Sensors

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We propose a polarized image defogging algorithm according to the sky segmentation results and transmission map optimization. Firstly, we propose a joint sky segmentation method based on scene polarization information, gradient information and light intensity information. This method can effectively segment the sky region and accurately estimate the global parameters such as atmospheric polarization degree and atmospheric light intensity at infinite distance. Then, the Gaussian filter is used to solve the light intensity map of the target, and the information of the polarization degree of the target is solved. Finally, based on the segmented sky region, a three-step transmission optimization method is proposed, which can effectively suppress the halo effect in the reconstructed image of large area sky region. Experimental results shows that defogging has a big improvement in the average gradient of the image and the grayscale standard deviation. Therefore, the proposed algorithm provides strong defogging and can improve the optical imaging quality in foggy scenes by restoring fog-free images.

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“…The implementation of the traditional Sobel operators are simple and fast. There are thicker edges identified with less accuracy due to the usage of only two direction templates such as horizontal and vertical templates [30] . So, in order to improve the edge features of the image to be diagnosed the Sobel operator direction templates can be extended by including few more directions.…”

Section: Proposed Edge Detection Approach 31 Proposed Sobel 12d Edge Detection Approachmentioning

confidence: 99%

Enhancement of digital chest images using a modified Sobel edge detection algorithm

Aishwarya

Joseph

2021

IJEECS

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Computed tomography (CT) images are an essential factor in the diagnosing procedure for various diseases affecting the internal organs. Edge detection can be used for the appropriate enhancement of the lung CT scan images for the diagnosis of the various interstitial lung diseases (ILD). In order to solve the issues of edge detection provided by the traditional Sobel operator, the paper proposes a Sobel 12D edge detection algorithm which uses the additional direction templates for the better identification of the edge details. First, the vertical and horizontal directions available in the traditional Sobel operator are extended to few more directions (a total of 12 directions) which enhances the edge extraction ability. Next part, compute the edge detected image using the Sobel 12D, Laplace, Prewitt, Robert’s Cross and Scharr operators for edge detection separately. It is followed by image fusion method which optimizes the edge detection by combining the edge detected images obtained using the Sobel 12D approach and the Laplace operator. The experimental results shows that the proposed algorithms generates a better detection of the edges than the other edge detection operators.

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Research on edge detection algorithm based on improved sobel operator

Cited by 33 publications

References 7 publications

Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization

Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization

Defogging Algorithm Based on Polarization Characteristics and Atmospheric Transmission Model

Enhancement of digital chest images using a modified Sobel edge detection algorithm

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