Infrared computer vision in non-destructive imaging: Sharp delineation of subsurface defect boundaries in enhanced truncated correlation photothermal coherence tomography images using K-means clustering

This work investigates the non-destructive detection of defects in thermal images of industrial materials based on segmentation of images generated using enhanced truncated-correlation photothermal coherence tomography (eTC-PCT). eTC-PCT is an active infrared thermography modality, which is being applied to the field of non-destructive testing (NDT) and in biomedical & dental thermophotonic imaging. In this report, we combine eTC-PCT with a computer vision algorithm to sharply delineate holes and manufacturing defects (cracks) inside industrial materials. To this end, the eTC-PCT reconstructed image is processed through three consecutive algorithm stages: A threshold selection filter is followed by filtered image segmentation using the K-means algorithm (clustering method) and the outcome is applied to the delineation of (otherwise blurred) discontinuity boundaries by means of the Canny edge detection algorithm. The role of each method is described and it is demonstrated that the combination of these three algorithms is optimal for achieving significant delineation enhancement (sharpness) of blind hole and crack boundaries in industrial materials.