Advanced approach of material region detections on fibre-reinforced concrete CT-scans

Abstract

Detections of material regions on CT-scans of solids are commonly treated manually by an expert. Although such manual detections have many advantages, some amount of human error is also incorporated. Moreover, expert opinions may vary significantly. We present an application of the k-means++ clustering as an alternative option to manual way of material area detections. k-means++ clustering is derived from k-means (the method of vector quantization, originally from signal processing), popular for cluster analysis in data mining and image processing communities. The algorithm s main advantages are its simple implementation and fast convergence to a local optimum of an objective function. We benchmark the suggested approach on transverse CT-scans of a fibre-reinforced concrete solid. Moreover, we introduce a technique for processing air distribution, such that the appropriate pixels detected as the pixels of air are converted into pixels representing concrete. The technique is based on the connected component algorithm. Benchmark and results of proposed method conclude the paper.