Segmentation of x-ray image for welding defects detection using an improved Chan-Vese model

Abdelkader, Rabah; Ramou, N.; Khorchef, Moahmmed; Chetih, Nabil; Boutiche, Yamina

doi:10.1016/j.matpr.2020.12.806

Cited by 13 publications

(6 citation statements)

References 10 publications

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“…In this case, A and B consist of voxels. This metric was used in [159] with the name Jaccard Index to evaluate the segmentation performance in welding defects inspection.…”

Section: ) Dice Coefficientmentioning

confidence: 99%

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learningbased object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

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“…In this case, A and B consist of voxels. This metric was used in [159] with the name Jaccard Index to evaluate the segmentation performance in welding defects inspection.…”

Section: ) Dice Coefficientmentioning

confidence: 99%

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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“…Before using this filter, the image was converted to grayscale. The mole or melanomas were separated at the segmentation stage using Chan-Vese's [15], i.e., based on the average pixel intensity. After that, post-processing was carried out using morphological filtering, i.e., opening and closing.…”

Section: Literature Reviewmentioning

confidence: 99%

Melanoma Detection using Convolutional Neural Network with Transfer Learning on Dermoscopic and Macroscopic Images

Millenia

Naufal²,

Siswantoro³

2022

J. Inf. Syst. Eng. Bus. Intell.

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Background: Melanoma is a skin cancer that starts when the melanocytes that produce the skin color pigment start to grow out of control and form a cancer. Detecting melanoma early before it spreads to the lymph nodes and other parts of the body is very important because it makes a big difference to the patient's 5-year life expectancy. Screening is the process of conducting a skin examination to suspect a mole is melanoma using dermoscopic or macroscopic images. However, manual screening takes a long time. Therefore, automatic melanoma detection is needed to speed up the melanoma detection process. The previous studies still have weakness because it has low precision or recall, which means the model cannot predict melanoma accurately. The distribution of melanoma and moles datasets is imbalanced where the number of melanomas is less than moles. In addition, in previous study, comparisons of several CNN transfer learning architectures have not been carried out on dermoscopic and macroscopic images. Objective: This study aims to detect melanoma using the Convolutional Neural Network (CNN) with transfer learning on dermoscopic and macroscopic melanoma images. CNN with Transfer learning is a popular method for classifying digital images with high accuracy. Methods: This study compares four CNN with transfer learning architectures, namely MobileNet, Xception, VGG16, and ResNet50 on dermoscopic and macroscopic image. This research also uses black-hat filtering and inpainting at the preprocessing stage to remove hair from the skin image. Results: MobileNet is the best model for classifying melanomas or moles in this experiment which has 83.86% of F1 score and 11 second of training time per epoch. Conclusion: MobileNet and Xception have high average F1 scores of 84.42% and 80.00%, so they can detect melanoma accurately even though the number of melanoma datasets is less than moles. Therefore, it can be concluded that MobileNet and Xception are suitable models for classifying melanomas and moles. However, MobileNet has the fastest training time per epoch which is 11 seconds. In the future, oversampling method can be implemented to balance the number of datasets to improve the performance of the classification model.

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“…Scholars have made exploration and research in relevant aspects. Representative works include Alwaheba et al [4,5] applied scanning contact potentiometry for defects detection, and for determining the location coordinates of defects in welded joints, Shen et al [6] proposed water flooding segmentation algorithm to weld defect detection, Chen et al [7] extracted X-ray weld image defects based on SUSAN algorithm, Li et al [8] identified weld defects based on independent component analysis, Yu et al [9,10] extracted weld centerline based on pyramid sparse network, Abdelkader et al [11] considered the characteristics of low contrast, poor quality, and uneven illumination of X-ray images, and studied weld defect extraction based on X-ray images. Ding et al [12] proposed the wavelet soft and hard threshold compromise denoising method, Patil et al [13] proposed the techniques of local binary pattern in which local binary code describing region, generating by multiplying threshold with specified weight to conforming pixel and summing up by grey-level co-occurrence matrix to extract statistical texture features, Boaretto et al [14] extracted potential defects based on feedforward multilayer perceptron with back propagation learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction Method of Weld Weak Defect Features for Ultra-High Voltage Equipment

Zheng¹,

Luo²,

Shen³

et al. 2023

Energy Engineering

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To solve the problems of low precision of weak feature extraction, heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage (UHV) equipment key parts, an automatic feature extraction algorithm is proposed. Firstly, the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method. Then, binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation. Finally, the automatic identification of weld defect area is realized based on the sequential traversal of binary tree. Several characteristic analysis dimensions are established for weld defects of UHV key parts, including defect area, perimeter, slenderness ratio, duty cycle, etc. The experiment using the weld detection image of the actual production site shows that the proposed method can effectively extract the weak feature information of weld defects and further provide reference for decision-making.

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Segmentation of x-ray image for welding defects detection using an improved Chan-Vese model

Cited by 13 publications

References 10 publications

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Melanoma Detection using Convolutional Neural Network with Transfer Learning on Dermoscopic and Macroscopic Images

Automatic Extraction Method of Weld Weak Defect Features for Ultra-High Voltage Equipment

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