Automatic segmentation of cracks in X-ray image based on OTSU and fuzzy sets

Shao, Zhonghua; Liang, Lihong

doi:10.1109/cisp.2010.5646794

Cited by 4 publications

(6 citation statements)

References 3 publications

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“…Motivated by recent developments and an improving availability of UAVs (Unmanned Aerial Vehicle) a growing number of applications have been developed over the last years, ranging from inspections of transmission lines, monitoring of fractionation towers in refineries, among others. However, we should take away irrelevant information associated notice correct segmentation result before diagnose [9]. Cracks on the concrete surface were one of the primitive implications of degradation of structure which is hard for the maintenance as well the continuous exposure would leads to the severe damage to the environment.…”

Section: Introductionmentioning

confidence: 99%

An Effective Surface Defect Detection Method Using Adaptive Thresholding Fused with PSO Algorithm

Aslam

Santhi

Ramasamy

et al. 2019

International Journal of Simulation: Systems, Science &Amp; Technology

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In Image processing and computer vision, the optimization technique aims at producing improved end result from a set of possible inputs. It is considered to be an exceptional method for the detection of defects from coated surfaces. In the presence of on-site inspections, it is practically impossible to detect surface defects such as small cracks on the titanium-coated surfaces of components. In this type of overlooking surface cracks, a new procedure must be discovered and it can effectively assist and improve the detection rates. We recommend titanium-coated surface crack detection system. The proposed method is depends upon the spatially clustered pixels with same grey levels. For image segmentation an Optimized Adaptive Thresholding (OAT) approach is proposed, in which Particle Swarm Optimization (PSO) is used along with adaptive thresholding. The LDP(Local Directional Pattern) approach is utilized for measuring the image edges gradient values in distinct ways and determine the pixel values in a various directions. LDP algorithm is usually applied to spot the image edge. If no edges are detected, then the nearby pixels are checked and joint to a region class using region growing algorithm. Finally the performance of the proposed method can be evaluated in terms of parameter calculations to achieve the high accuracy, specificity and sensitivity of the crack image.

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Section: Introductionmentioning

confidence: 99%

An Effective Surface Defect Detection Method Using Adaptive Thresholding Fused with PSO Algorithm

Aslam

Santhi

Ramasamy

et al. 2019

International Journal of Simulation: Systems, Science &Amp; Technology

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show abstract

“…Automatic segmentation of line-like regions in the X-ray/CT images is a very important stage for many automatic recognition systems, such as automatic crack recognitions systems [1,2,3,4], automatic lung disease recognition in chest CT images [5,6,7]. For these automatic systems, to detect the line-like regions (such as cracks in the X-ray image, vessels in the chest CT images) is a very critical stage for the automatic recognition.…”

Section: Introductionmentioning

confidence: 99%

A Robust Line Filter for Automatic X-Ray/CT Image Segmentation

Peng

Nam²,

Gan

et al. 2014

AMM

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Automatic segmentation of the line-like regions plays a very important role in the automatic recognition system, such as automatic cracks recognition in X-ray images, automatic vessels segmentation in CT images. In order to automatically segment line-like regions in the X-ray/CT images, this paper presents a robust line filter based on the local gray level variation and multiscale analysis. The proposed line filter makes usage of the local gray level and its local variation to enhance line-like regions in the X-ray/CT image, which can well overcome the problems of the image noises and non-uniform intensity of the images. For detecting various sizes of line-like regions, an image pyramid is constructed based on different neighboring distances, which enables the proposed filter to analyze different sizes of regions independently. Experimental results showed that the proposed line filter can well segment various sizes of line-like regions in the X-ray/CT images, which are with image noises and non-uniform intensity problems.

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“…In order to detect cracks in X-ray images automatically, Shao et al [3] proposed a segmentation method based on double-thresholding and fuzzy set theory to automatically detect cracks from the X-ray images. The system includes three main components: preprocessing, thresholding segmentation and post-processing.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the inspection results can vary with respect to the skill and alertness of the operator. Therefore, automatic inspection systems [3][4][5] based on image processing have been developed to improve inspection results and reduce the workload of operators.…”

Section: Introductionmentioning

confidence: 99%

“…With the ability to penetrate materials and be attenuated according to the material's thickness and density, X-ray inspection systems [1][2][3] are widely used for the identification and evaluation of internal cracks in many industrial products nowadays. In a conventional inspection system, the X-ray images are manually examined by radiologists in order to check if there are cracks in the product.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Crack Filter Based on Local Gray Level Variation and Multiscale Analysis for Automatic Crack Detection in X-ray Images

Peng¹,

Nam²

2016

Journal of Electrical Engineering and Technology

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-Internal cracks in products are invisible and can lead to fatal crashes or damage. Since Xrays can penetrate materials and be attenuated according to the material's thickness and density, they have rapidly become the accepted technology for non-destructive inspection of internal cracks. This paper presents a robust crack filter based on local gray level variation and multiscale analysis for automatic detection of cracks in X-ray images. The proposed filter takes advantage of the image gray level and its local variations to detect cracks in the X-ray image. To overcome the problems of image noise and the non-uniform intensity of the X-ray image, a new method of estimating the local gray level variation is proposed in this paper. In order to detect various sizes of crack, this paper proposes using different neighboring distances to construct an image pyramid for multiscale analysis. By use of local gray level variation and multiscale analysis, the proposed crack filter is able to detect cracks of various sizes in X-ray images while contending with the problems of noise and non-uniform intensity. Experimental results show that the proposed crack filter outperforms the Gaussian model based crack filter and the LBP model based method in terms of detection accuracy, false detection ratio and processing speed.

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Automatic segmentation of cracks in X-ray image based on OTSU and fuzzy sets

Cited by 4 publications

References 3 publications

An Effective Surface Defect Detection Method Using Adaptive Thresholding Fused with PSO Algorithm

An Effective Surface Defect Detection Method Using Adaptive Thresholding Fused with PSO Algorithm

A Robust Line Filter for Automatic X-Ray/CT Image Segmentation

A Robust Crack Filter Based on Local Gray Level Variation and Multiscale Analysis for Automatic Crack Detection in X-ray Images

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