A robust segmentation approach based on feature analysis for defect detection in aluminium castings X-ray images
Résumé
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.