An improved image processing technique for asphalt concrete X-ray CT images

This paper presents a comparative evaluation of two image segmentation techniques for processing asphalt concrete microstructure images obtained with X-ray computed tomography (CT). These are the adaptive enhancement-based thresholding algorithm (AETA) and the watershed segmentation embedded into the volumetric-based thresholding algorithm (VTA). Both these methods were used to process the X-ray CT images of nine asphalt concretes. These consisted of three mix types, each prepared with three aggregate types. The mix designs included a coarse matrix high-binder Type C mix, a gap-graded porous friction course mix, and a fine-graded Superpave Type C (Superpave) mix. The three aggregate types included hard limestone, granite, and soft limestone. All mixtures were prepared with a PG 76-22 modified binder. The comparison of the two methods was carried out both visually and quantitatively. The later was accomplished by comparing the gradation estimated from the images using purpose-designed software to the gradation obtained from mechanical sieving. The results show that the inherent over-segmentation problem with the VTA technique is effectively reduced using the AETA method. Overall, the AETA method outperforms the VTA watershed image segmentation method by producing better separation between connected and overlapping aggregates for the nine asphalt concretes included in the study. Its drawback is that it does not preserve the volumetric properties of the mixtures, as done by the VTA technique.