Edge detection algorithm of medical image based on Canny operator

Xu, Zhihai; Ji, Xiaoqiang; Mei-jiao, Wang; Sun, Xiaobing

doi:10.1088/1742-6596/1955/1/012080

Cited by 29 publications

(14 citation statements)

References 4 publications

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“…Simply with visual observation, the results obtained by neuro-fuzzy techniques are notably better than those by Canny operator (Fig. 3G) ( 31 ) and self-organizing map (SOM; Fig. 3H) ( 32 ).…”

Section: Resultsmentioning

confidence: 83%

Stochastic neuro-fuzzy system implemented in memristor crossbar arrays

Shi,

Zhang,

Cui

et al. 2024

Sci. Adv.

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Neuro-symbolic artificial intelligence has garnered considerable attention amid increasing industry demands for high-performance neural networks that are interpretable and adaptable to previously unknown problem domains with minimal reconfiguration. However, implementing neuro-symbolic hardware is challenging due to the complexity in symbolic knowledge representation and calculation. We experimentally demonstrated a memristor-based neuro-fuzzy hardware based on TiN/TaO x /HfO x /TiN chips that is superior to its silicon-based counterpart in terms of throughput and energy efficiency by using array topological structure for knowledge representation and physical laws for computing. Intrinsic memristor variability is fully exploited to increase robustness in knowledge representation. A hybrid in situ training strategy is proposed for error minimizing in training. The hardware adapts easier to a previously unknown environment, achieving ~6.6 times faster convergence and ~6 times lower error than deep learning. The hardware energy efficiency is over two orders of magnitude greater than field-programmable gate arrays. This research greatly extends the capability of memristor-based neuromorphic computing systems in artificial intelligence.

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Section: Resultsmentioning

confidence: 83%

Stochastic neuro-fuzzy system implemented in memristor crossbar arrays

Shi,

Zhang,

Cui

et al. 2024

Sci. Adv.

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“…Mean-square-error (MSE) is a significant index to judge the specific differences between two images [38] . To test the effect of encryption and decryption algorithm, MSE is calculated as [39]…”

Section: Mean Square Error and Peak Signal-to-noise Ratio Analysismentioning

confidence: 99%

Medical Image Encryption Based on Josephus Traversing and Hyperchaotic Lorenz System

Yang

Zhang

Bai

et al. 2022

J. Shanghai Jiaotong Univ. (Sci.)

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This study proposes a new medical image encryption scheme based on Josephus traversing and hyperchaotic Lorenz system. First, a chaotic sequence is generated through hyperchaotic system. This hyperchaotic sequence is used in the scrambling and diffusion stages of the algorithm. Second, in the scrambling process, the image is initially confused by Josephus scrambling, and then the image is further confused by Arnold map. Finally, generated hyperchaos sequence and exclusive OR operation is used for the image to carry on the positive and reverse diffusion to change the pixel value of the image and further hide the effective information of the image. In addition, the information of the plaintext image is used to generate keys used in the algorithm, which increases the ability of resisting plaintext attack. Experimental results and security analysis show that the scheme can effectively hide plaintext image information according to the characteristics of medical images, and is resistant to common types of attacks. In addition, this scheme performs well in the experiments of robustness, which shows that the scheme can solve the problem of image damage in telemedicine. It has a positive significance for the future research.

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“…In order to calculate the gradient, the derivative of the Gaussian filter is used. To detect the strong edge and the weak edge, respectively, the Canny operator uses two thresholds [18]. e traditional algorithm of Canny edge detection is used in grayscale images as this algorithm is not able to deal with color images, so there is great difficulty in determining parameters adaptively [19].…”

Section: Canny Edge Detectormentioning

confidence: 99%

X-Ray Lung Image Classification Using a Canny Edge Detector

Jadwaa

2022

Journal of Electrical and Computer Engineering

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The medical imaging technique is used in order to obtain a tissue image of a specific part of the human body without any surgical intervention. The presence of differences in the clinical experiences of a section of doctors or doctors in general can lead to discrepancies in the analysis and understanding of medical images and thus affects the accuracy of the diagnosis for the patient’s condition. The use of a medical imaging system for reliable diagnosis through the use of the computer will lead to high accuracy in diagnosis. For this reason, the need to improve the special performance of systems that perform computer-aided diagnosis used in the medical imaging process has increased special performance of the computer-aided diagnostic systems used in the medical imaging process. The medical image classification technique has the ability to perform a preliminary analysis as well as an understanding of medical images and also can identify the affected parts of the human body, which leads to helping doctors in the process of optimal diagnosis. The process of classifying medical images needs to extract the features of the image so that the classification process is carried out with high accuracy, and one of these features is detecting the edges of the image using a Canny edge detector. This is what the author performed in the research, and the experimental results show the effectiveness and goodness of this method.

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Edge detection algorithm of medical image based on Canny operator

Cited by 29 publications

References 4 publications

Stochastic neuro-fuzzy system implemented in memristor crossbar arrays

Stochastic neuro-fuzzy system implemented in memristor crossbar arrays

Medical Image Encryption Based on Josephus Traversing and Hyperchaotic Lorenz System

X-Ray Lung Image Classification Using a Canny Edge Detector

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