基于自适应阈值的Canny算法在MRI边缘检测中的应用

doi:10.13229/j.cnki.jdxbgxb20200839

Abstract

Abstract:

As a classic multi-level optimization algorithm， the Canny algorithm is widely used in MRI edge detection. MRI has the limitations of uneven gray density and low contrast. This paper uses lumbar disc MRI as an example to propose an improved Canny algorithm for optimization in edge detection. First， the image contrast is enhanced， and on this basis， the median filtering is introduced to effectively process impulse noise for preprocessing. Then， for the problem of insufficient edge extraction accuracy， the gradient direction template is increased to obtain the gradient amplitude and direction. For the excessive number of false edges and edge discontinuities， the gradient intensity information calculation method is used to achieve threshold adaptation. Finally， the image enhancement is performed again through the histogram normalization. This paper uses four evaluation criteria of PSNR， SSIM， MSE and algorithm running time to verify the traditional algorithm， the existing algorithm and the improved algorithm in this paper. The results show that the improved Canny algorithm significantly improves the accuracy of MRI detection， effectively reduces the false edges， and it is adaptive. The results of this article have certain reference significance for MRI in medical image processing.

Key words: image processing, edge detection, Canny algorithm, MR image

CLC Number:

TP391.41

Jian LI,Kong-yu LIU,Xian-sheng REN,Qi XIONG,Xue-feng DOU. Application of canny algorithm based on adaptive threshold in MR Image edge detection[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 712-719.

Figures/Tables 11

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算法	峰值信噪比 (PSNR)	均方误差 (MSE)	结构相似性 (SSIM)	运行时间/s
Sobel算子	11.352	3523.31	0.6000	0.364
Laplacian算子	11.428	4679.29	0.5100	0.342
Otsu	12.241	3715.31	0.4400	0.447
传统Canny	11.031	5128.44	0.4300	0.064
文献[1]	11.307	6058.41	0.4929	0.331
文献[19]	10.582	6250.43	0.4310	8.288
本文	13.568	2860.17	0.7500	0.196

Application of canny algorithm based on adaptive threshold in MR Image edge detection

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