An automatic image analysis methodology for the measurement of droplet size distributions in liquid–liquid dispersion: round object detection

Abstract

This article presents an efficient and economical automatic image analysis technique for the droplet characterization in a liquid–liquid dispersion. The methodology employs a combination of the Satoshi Suzuki's [Topological structural analysis of digitized binary images by border following. Comput Vis Graph Image Process. 1985;30:32–46] find contours algorithm and the method of minimal enclosing circle identification, proposed by Emo Welzl [Smallest enclosing disks (balls and ellipsoids). Berlin, Heidelberg: Springer; 1991. p. 359–370. chapter 24], to achieve the objectives. The round object detection algorithm has been designed for the identification and verification of correct droplets in the mixture which helped to increase the accuracy of automatic detection. Tests have been performed on various sets of images obtained during several emulsification processes and contain examples of droplets which differ in size, density, volume and appearance etc. An effective communication between the two methodologies and newly introduced algorithms demonstrated an accuracy of 90% or above in the measurement of droplet size distribution and Sauter mean diameters through an automatic vision-based system.