A method for segmentation assessment is proposed. The technique is based on a comparison of the segmentation produced by an algorithm with an ideal segmentation. The procedure to obtain the ideal segmentation is described in detail. Uncertainty regarding the edge positions is accounted for in the discrepancy calculation of each edge using fuzzy reasoning. The uncertainty measurement consists of a generalization, using fuzzy membership functions, of the similarity metrics used by well-known assessment methods. Several alternatives for the fuzzy membership functions, based on statistical properties of the possible positions of each edge, are defined. The proposed uncertainty measurement can be easily applied to other well-known methods. Finally, the segmentation assessment method is used to determine the best segmentation algorithm for thermographic images, and also to tune the optimum parameters of each algorithm.