Automated analysis of wood anatomical sections is o f great interest in understanding the growth and development o f plants. In this paper, we propose a novel method to characterize the cell organization in light microscopic wood section images. It aims to identify automatically the cell file in a context of mass treatment. The originality of the proposed method is our cell classification process. Unlike many supervised methods, our method is self conditioned, based on a decision tree which thresholds are automatically evaluated according to specific biological characteristics of each image. In order to evaluate the performances of the proposed system and allow the certification of the cell line detection, we introduced indices of quality characterizing the accuracy o f results and parameters of these results. Those are related to topological and geometrical characters of the cell file at both global and local scales. Moreover, we propose an index of certainty for selective results exploitation in further statistical studies. The proposed method was is implemented as a plugin for ImageJ. Tests hold on various wood section well contrasted images show good results in terms of cell file detection and process speed.