Spatially-varying signal content can be effectively modeled using amplitude modulation-frequency modulation (AM-FM) representations. The AM-FM representation allow us to extract instantaneous amplitude (IA) and instantaneous frequency (IF) components that can be used to measure non-stationary content in biomedical images and videos. This paper introduces a new method for estimating the IA and the IF based on a quasi-local method (QLM). We provide an extensive comparison of AM-FM demodulation approaches based on QLM and a quasi-eigenfunction approximation method using three different filter-banks: (i) a separable, equiripple design, (ii) a Gabor filter bank, and (iii) a directional filter bank approach based on the Contourlet transform. The results document that the use of the new QLM method with an equiripple filter bank design gave the best IF magnitude estimates for a synthetic image. The new QLM method is then applied to a multi-site schizophrenia dataset (N=307). The dataset included structure magnetic resonance images from healthy controls and patients diagnosed with schizophrenia. The IF magnitude is shown to be less sensitive to variations across sites as opposed to the standard use of SMRI images that suffered from significant dependency on the scanner configurations on different collection sites. Furthermore, the regions of interest identified through the use of the IF magnitude are in agreement with previous studies.