A semi-automatic approach to measurement of pancreatic endocrine volume tissue density

This paper present a reliable, fast and efficient method for measuring the volume density of pancreatic endocrine volume density. The algorithm segmentates digitized images in three different classes: the endocrine (En), exocrine (Ex) and artifact (At) components. A statistical classifier based on the k-Nearest Neighbour (k-NN) decision rule in the RGB color space was compared with a standard point counting technique. The k-NN rule classifies other pixels in the class that is mostly represented among the k nearest training samples in the RGB space, which is efficiently implemented with a fast k-distance transform algorithm. All extracted areas were quantified in absolute (μm²) and relative (%) values. The different tissues were point counting determined and their quantifications statistically compared with those obtained semi-automatically. All analyses were performed by an expert pathologist and showed no significant differences between the two approaches.