Describes an automated approach for analyzing complex scenes obtained from digitized scanning electron microscopy (SEM) images of lung bio-dissolution preparations. Such images are used in the evaluation of airborne fiberglass distributions. They present varying densities of isolated, overlapping, and crossing fibers and other objects that should be individually identified and measured. A polygonal approximation model for each detected object in the scene is used to obtain a simplified description. After detecting complex objects, such as crossing fibers, an unsupervised neural network is used to resolve the ambiguities and separate the objects. The method performs satisfactorily under different conditions of fiber density and background clutter.
['Oscar Yáñez-Suárez', 'Mahmood R. Azimi-Sadjadi']
Automated analysis of complex scenes of airborne fiberglass preparations for scanning electron microscopy imagery