The imaging performance of fluorescence molecular tomography (FMT) improves when information from the underlying anatomy is incorporated into the inversion scheme, in the form of priors. The requirement for incorporation of priors has recently driven the development of hybrid FMT systems coupled to other modalities, such as X-ray CT and MRI. A critical methodological aspect in this modality relates to the particular method selected to incorporate prior information obtained from the anatomical imaging modality into the FMT inversion. We propose herein a new approach for utilizing prior information, which preferentially minimizes residual errors associated with measurements that better describe the anatomical segments considered. This preferential minimization was realized using a weighted least square (WLS) approach, where the weights were optimized using a Mamdani-type fuzzy inference system. The method of priors introduced herein was deployed as a two-step structured regularization approach and was verified with experimental measurements from phantoms as well as ex vivo and in vivo animal studies. The results demonstrate accurate performance and minimization of reconstruction bias, without requiring user input for setting the regularization parameters. As such, the proposed method offers significant progress in incorporation of anatomical priors in FMT and, as a result, in realization of the full potential of hybrid FMT.