Automatic segmentation and measurement of axons in microscopic images.

We propose a method for the automatic segmentation, recognition and measurement of neuronal fibers in microscopic images of nerves. This permits a quantitative analysis of the distribution of the areas of the fibers, while nowadays such morphometrical methods are limited by the practical impossibility to process large amounts of fibers in histological routine. First, the image is thresholded to provide a coarse classification between myelin (black) and non-myelin (white) pixels. The resulting binary image is simplified using connected morphological operators. These operators simplify the zonal graph, whose vertices are the connected areas of the binary image. An appropriate set of semantic rules allow us to identify a number of white areas as axon candidates, some of which are isolated, some of which are connected. To separate connected fibers - candidates sharing the same neighboring black area - we evaluate the thickness of the myelin ring around each candidate area through Euclidean distance transformation by propagation with a stopping criterion on the pixels in the propagation front. Finally, properties of each detected fibers are computed and false alarms are suppressed. The computational cost of the method is evaluated and the robustness of the method is assessed by comparison to the manual procedure. We conclude that the method is fast and accurate for our purpose.