Multi-scale and first derivative analysis for edge detection in TEM images

Transmission electron microscope images of biological membranes are difficult to segment because they are low-contrasted images with heterogeneous gray levels. Added to that are the many possible types of membranes, the variable degree of aggregation, and the negative staining of the sample. We therefore develop a multi-scale approach to detect the edges at the appropriate scales. For these images, the study of the amplitude of the first derivative through the scales simplifies the feature tracking and the scale selection. A scale-adapted threshold is automatically applied to gradient images to progressively segment edges through the scales. The edges found at the different scales are then combined into a gradient-like image. The watershed algorithm is finally applied to segment the image into homogeneous regions, automatically selecting the edges found at the finest resolution.