This thesis is part of a broader research project which aims to analyze the potential migration of cancer cells. As part of this doctorate, we are interested in the use of image processing to count and classify cells present in an image acquired using a microscope. The partner biologists of this project study the influence of the environment on the migratory behavior of cancer cells from cell cultures grown on different cancer cell lines. The processing of biological images has so far resulted in a significant number of publications, but in the case discussed here, since the protocol for the acquisition of images acquired was not fixed, the challenge was to propose a chain of adaptive processing that does not constrain the biologists in their research. Four steps are detailed in this paper. The first concerns the definition of pre-processing steps to homogenize the conditions of acquisition. The choice to use the image of standard deviations rather than the brightness is one of the results of this first part. The second step is to count the number of cells present in the image. An original filter, the so-called "halo" filter, that reinforces the centre of the cells in order to facilitate counting, has been proposed. A statistical validation step of the centres affords more reliability to the result. The stage of image segmentation, undoubtedly the most difficult, constitutes the third part of this work. This is a matter of extracting images each containing a single cell. The choice of segmentation algorithm was that of the "watershed", but it was necessary to adapt this algorithm to the context of images included in this study. The proposal to use a map of probabilities as input yielded a segmentation closer to the edges of cells. As against this method leads to an over-segmentation must be reduced in order to move towards the goal: "one region = one cell". For this algorithm the concept of using a cumulative hierarchy based on mathematical morphology has been developed. It allows the aggregation of adjacent regions by working on a tree representation of these regions and their associated level. A comparison of the results obtained by this method with those proposed by other approaches to limit over-segmentation has allowed us to prove the effectiveness of the proposed approach. The final step of this work consists in the classification of cells. Three classes were identified: i spread cells (mesenchymal migration), "blebbing" round cells (amoeboid migration) and "smooth" round cells (intermediate stage of the migration modes). On each imagette obtained at the end of the segmentation step, intensity, morphological and textural features were calculated. An initial analysis of these features has allowed us to develop a classification strategy, namely to first separate the round cells from spread cells, and then separate the "smooth" and "blebbing" round cells. For this we divide the parameters into two sets that will be used successively in two the stages of classification. Several classification algorithms were tested, to retain in the end, the use of two neural networks to obtain over 80% of good classification between long cells and round cells, and nearly 90% of good classification between "smooth" and "blebbing" round cells.
