Edge Detection for Industrial CT Image Based on GuidedFiltering and Non-subsampled Shearlet Transform
Received date: 2015-11-10
Revised date: 2015-12-24
Online published: 2016-07-30
As existing image edge detection algorithms cannot accurately detect edges from noisy industrial computed tomography (CT) images, a robust edge detection algorithm capable of preserving fine edges is proposed. Instead of Gaussian filtering, guided filtering is used in image pre-processing for edge detection to avoid edge destruction of the Canny algorithm. Having obtained the preliminary detection result, non-subsampled shearlet transform (NSST) is applied for image decomposition. High-frequency coefficients of various scales in different directions containing edges and details are extracted. Modulus maximum detection is performed on the coefficients in each direction, and the maximum modulus values are adjusted depending on the property of coefficients of the edge points under different decomposing conditions. By setting the low-frequency coefficients to zero, inverse NSST is performed to get the high-frequency edge detection result. Finally, the preliminary result and the high-frequency detection result are combined. The final edge map is obtained with mathematical morphology. Experiments are performed and detection results are compared with those of classical Canny algorithm and several recent and similar edge detection algorithms. The proposed algorithm shows better edge preserving property, higher edge integrity and accuracy. An average increase of 12% of the figure of merit (FOM) indicator is achieved. The proposed edge detection algorithm provides a better edge detection scheme for industrial CT nondestructive testing systems.
MENG Tian-liang, WU Yi-quan, WU Shi-hua . Edge Detection for Industrial CT Image Based on GuidedFiltering and Non-subsampled Shearlet Transform[J]. Journal of Applied Sciences, 2016 , 34(4) : 405 -416 . DOI: 10.3969/j.issn.0255-8297.2016.04.006
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