The objective of our work is to develop a tool for automatic analysis of 2D membrane protein crystal images
in Transmission Electron Microscopy (TEM). The success of crystallization experiments is evaluated at high
magnification. The crystalline structure of a membrane can be observed when no other membranes are superposed.
It is therefore necessary to identify mono-layer membranes. In this paper we introduce an algorithm
that determines the stacking-level of membranes. Our method determines a quantum, a gray-level quantity that
is characteristic of a non-stacked membrane. In this way we are able to label each region qualitatively and
construct a stacking-level map that distinguishes from non-stacked to up to four-level stacked membranes. This
map provides the regions that will trigger a new image acquisition at higher magnification.
Images of biological objects in transmission electron microscopy (TEM) are particularly noisy and low contrasted,
making their processing a challenging task to accomplish. During these last years, several software tools were
conceived for the automatic or semi-automatic acquisition of TEM images. However, tools for the automatic analysis of
these images are still rare. Our study concerns in particular the automatic identification of artificial membranes at
medium magnification for the control of an electron microscope. We recently proposed a segmentation strategy in order
to detect the regions of interest. In this paper, we introduce a complementary technique to improve contour recognition
by a statistical validation algorithm. Our technique explores the profile transition between two objects. A transition is
validated if there exists a gradient orthogonal to the contour that is statistically significant.