Abstract :
Drop-shape techniques, such as axisymmetric drop-shape analysis (ADSA), have been widely used to measure surface
tension. In the current schemes, theoretical curves are fitted to the experimental profiles by adjusting the value of surface tension.
The best match between theoretical and experimental profiles identifies the surface tension of the drop. Extracting the
experimental drop profile using edge detection, is an important part of the current drop-shape techniques. However, edge
detections fail when acquisition of sharp images is not possible due to experimental or optical limitations. A new drop-shape
approach is presented, which eliminates the need for the edge detection and provides a wider range of applicability. The new
methodology, called theoretical image fitting analysis (TIFA), generates theoretical images of the drop and forms an error
function that describes the pixel-by-pixel deviation of the theoretical image from the experimental one. Taking surface tension as
an adjustable parameter, TIFA minimizes the error function, i.e. fits the theoretical image to the experimental one. The validity of
the new methodology is examined by comparing the results with those of ADSA. Using the new methodology it is finally
possible to enhance the study of the surface tension of lung surfactants at higher concentrations. Due to the opaqueness of the
solution, such studies were limited to the low concentrations of surfactants heretofore.
