Circular discontinuities detection in welded joints using Circular Hough Transform

Yazid, Hafizal; Yazid, Haniza; Harun, Mohamad Haniff; Mohd, Shukri Bin; Mohamed, Abdul Aziz; Rizon, M.; Sayuti, Shaharudin

doi:10.1016/j.ndteint.2007.05.004

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…By carrying out the line profile diametrically at full width half maximum (FWHM), the diameter was estimated as 16.25 ± 0.25 mm and the calibrated pixel size was 0.25 mm. Hence, the diameter obtained by the automatic sizing approach using the area gave a closer estimate to the actual size of the object in comparison to the estimate made by the line profile and perimeter [19] .…”

Section: Sizing Of Circular Featuresmentioning

confidence: 72%

“…Yazid et al [19] described the application of a separability mask and the circular Hough transform (CHT) to the detection of circular objects in radiographic images of weld joints where the success rate was 81.82%, which indicated that their system was suitable for the detection of circular features in the weld zone. In the present paper, the Hough transform is used to isolate circular-shaped pores, which appear as rounded indications on the radiographs of the tube-to-tube weld joints of the steam generators of a prototype fast breeder reactor (PFBR).…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection and sizing of pores from radiographic images using the Hough transform

Arunmuthu¹,

Kumar²,

Saravanan³

et al. 2010

Insight - Non-Destructive Testing and Condition Monitoring

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We use a pattern recognition approach to assist the human interpreter in evaluating the quality of welds from radiographic images, especially tube-to-tubesheet weld joints in the steam generator assembly of the prototype fast breeder reactor (PFBR), that require stringent inspection as the joints separate water and steam. In this paper, the circular Hough transform is used for automatic detection of circular discontinuities that are present in the weld joints, and we use morphological parameters to classify these defects in terms of their size. Using this approach, a success rate of 99% detection of circular discontinuities is achieved, by segmenting the image into smaller regions of interest and using the circular Hough transform along with morphological parameters.

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Section: Sizing Of Circular Featuresmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Automatic detection and sizing of pores from radiographic images using the Hough transform

Arunmuthu¹,

Kumar²,

Saravanan³

et al. 2010

Insight - Non-Destructive Testing and Condition Monitoring

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“…Many methods have been suggested for identifying the round objects. Circular Hough transform (CHT), which proposed by Duda et al [ 27 ], is one of the most successful method [ 28 ] for detection of round objects on the images. In this study, CHT operations are used for candidate nodule detection.…”

Section: Architecture Of the Computer-aided Pipelinementioning

confidence: 99%

A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans

Dandıl

2018

Journal of Healthcare Engineering

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Lung cancer is one of the most common cancer types. For the survival of the patient, early detection of lung cancer with the best treatment method is crucial. In this study, we propose a novel computer-aided pipeline on computed tomography (CT) scans for early diagnosis of lung cancer thanks to the classification of benign and malignant nodules. The proposed pipeline is composed of four stages. In preprocessing steps, CT images are enhanced, and lung volumes are extracted from the image with the help of a novel method called lung volume extraction method (LUVEM). The significance of the proposed pipeline is using LUVEM for extracting lung region. In nodule detection stage, candidate nodules are determined according to the circular Hough transform- (CHT-) based method. Then, lung nodules are segmented with self-organizing maps (SOM). In feature computation stage, intensity, shape, texture, energy, and combined features are used for feature extraction, and principal component analysis (PCA) is used for feature reduction step. In the final stage, probabilistic neural network (PNN) classifies benign and malign nodules. According to the experiments performed on our dataset, the proposed pipeline system can classify benign and malign nodules with 95.91% accuracy, 97.42% sensitivity, and 94.24% specificity. Even in cases of small-sized nodules (3–10 mm), the proposed system can determine the nodule type with 94.68% accuracy.

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“…In the medical field itself, another application is classification and localization of diabetic related eyes diseases. Other applications include detection of car wheels, detection of cross-section of composite yarn, aorta detection in ultra sound pictures, atom structure detection and various other industrial applications [1].…”

Section: Introductionmentioning

confidence: 99%

Improved Hough transform for fast Iris detection

Bhatia

Chhabra

2010

2010 2nd International Conference on Signal Processing Systems

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Objectives: an important part of digital image processing is recognition of patterns. Iris recognition is an important application of the same. As a result of the fact that CHT lacks speed due to its high time complexity, it is useful to recognize valid regions and make these regions the only regions to process. Extracting a valid region from the image not only reduces the image storage and the quantity of operations, it also improves the speed of the process. The objective of the work is to describe a method for enhancing the speed of extracting circle i.e. iris from the image without compromising the accuracy of the technique. A modified version of Hierarchical Hough transform is also proposed which further reduces the time taken to detect iris. An exhaustive analysis is conducted for a large number of iris images and comparison results are shown. Method: The HT space is limited by dividing it into nine equal parts and then the center most space is extracted as the valid region. Then CHT and HHT transformations are applied on the valid region.Results: About 88.77% of maximum improvement and 87.989 % of average improvement is recorded in time of execution of proposed CHT in comparison to existing CHT. Whereas, run time of HHT and proposed HHT when compared, results show that the maximum performance rate is 86.649% and average performance rate is 46.81%. Conclusion: in case of HT base detection methods, execution speed is directly proportional to the number of edge pixels in input image. Restricting the number of pixels provides the advantage of considerably simplified computations, and hence faster algorithms. The work documents that an aggregate comparison of proposed transformations with existing transformations has achieved a faster yet accurate method to recognize iris.

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Circular discontinuities detection in welded joints using Circular Hough Transform

Cited by 10 publications

References 7 publications

Automatic detection and sizing of pores from radiographic images using the Hough transform

Automatic detection and sizing of pores from radiographic images using the Hough transform

A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans

Improved Hough transform for fast Iris detection

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