A robust segmentation approach based on analysis of features for defect detection in X-ray images of aluminium castings
A robust image processing algorithm has been developed for detection of small and low contrasted defects, adapted to X-ray images of castings having a non-uniform background. The sensitivity to small defects is obtained at the expense of a high false alarm rate. We present in this paper a feature extraction approach to complement the image processing, reducing the false alarms rate, while keeping a high defect detection rate, which is impossible by image processing techniques alone. ROC curves show a very good performance by using a new feature parameter, called Defect Confidence Index, combining three parameters and taking into account the fact that X-ray grey-levels follow a statistical normal law. Results are shown on a set of 684 images, involving 59 defects, on which we obtained a 100% detection rate without any false alarm.
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Document Type: Research Article
Publication date: October 1, 2007
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- Official Journal of The British Institute of Non-Destructive Testing - includes original research and devlopment papers, technical and scientific reviews and case studies in the fields of NDT and CM.
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