A robust segmentation approach based on analysis of features for defect detection in X-ray images of aluminium castings

G. Lecomte, V. Kaftandjian, Emmanuelle Cendre, D. Babot

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    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.
    Original languageEnglish
    JournalInsight - Non-Destructive Testing and Condition Monitoring
    Volume49
    Issue number10
    Pages (from-to)572-577
    ISSN1354-2575
    DOIs
    Publication statusPublished - 2007

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