Spatial filtering is a ubiquitously used image processing approach to reduce noise, and frequently part of image processing pipelines. The most commonly used function is the Gaussian. Recently, a generalization of the Gaussian function consistent with nonadditive statistics was proposed. Although generalized Gaussian has been used for image filtering, no study assessed its performance for medical images. Here, we present two classes of Q-Gaussian filters as noise reduction methods. We evaluated filter performance for magnetic resonance images (MRI) in cerebral, thoracic and abdominal regions. Fractal dimension estimations from images were paired with filter effectiveness. Results showed that Q-Gaussian filters have improved filtering effective gain, when compared to classical Gaussian filtering. Furthermore, it is observed filter gain dependence with fractal dimension. The obtained results suggest that the Q-Gaussian filters are better for noise reduction than classic Gaussian filter when dealing with fractal MRI or fractal noise.