This application note details how imaging particle analysis automatically characterizes fibrous particles using the actual image and shape in addition to ESD. This allows each, individual fiber to be accurately measured for the degree of curl, straightness, length, width and other parameters, and to be automatically differentiated from each other.
This paper explains how adjusting the gray-scale threshold value using both darker and lighter pixels reveals transparent particles that would otherwise escape detection to ensure accurate count and concentration calculations. Protein agglomerate images demonstrate how properly varying threshold documents presence of one, large particle instead of several small particles.
This application note explains how counting and characterizing particles based upon their actual size and shape instead of by ESD enables protein agglomerates to be automatically differentiated from silicone droplets, air bubbles and foreign matter.