Abstract Image fusion is an interesting processing task that has reached great significance for medical image analysis. In general, the combination of medical images coming from different modalities is a common practice that significantly helps in the process of diagnosis and detection of several diseases. In this work, we present a novel method for image fusion based on the Hermite transform which consists of a powerful tool that projects an input image into the space defined by the Hermite polynomials. The proposed approach is performed in three main stages. 1) The HT is applied to the input images, 2) The resulting coefficients are fused using the maximum and average intensity rules, and 3) The inverse HT is performed to obtain the final fused image. The method is applied and evaluated using several single photon emission computed tomography and computed tomography studies taken for bone structures. Typical metrics were used to assess the proposed framework. We demonstrate that this methodology is able to efficiently fuse images coming from different modalities, particularly, nuclear medicine and x-ray tomographic techniques. With the Hermite transform, image features are successfully extracted which becomes fundamental in the process of image fusing.