The purpose of this research was to introduce and analyze a technique for enhancing elasticity image quality using locally adaptive Gaussian filtering. To assess the performance of this filtering method for reconstructing images with missing or degraded data, heterogeneous images were simulated with circular regions of intensity twice that of the surrounding material. Missing pixel data was introduced by thresholding a uniformly distributed noise matrix. Results demonstrate locally adaptive Gaussian filtering accurately reconstructs the original image while preserving boundary detail. To further analyze the performance of this filtering technique, multiple local image regions were suppressed and normally distributed noise superimposed. Consequently, locally adaptive Gaussian filtering is capable of reconstructing local missing data whereas both median and conventional Gaussian filtering fails. Using compressional elastographic experimental data, results illustrate that locally adaptive Gaussian filtering is capable of minimizing decorrelation noise artifacts while preserving lesion boundaries. Additionally, results obtained using vibrational shear velocity sonoelastography further illustrate the ability of locally adaptive Gaussian filtering to enhance image quality by minimizing estimator noise degradation in comparison to conventional spatial filtering techniques. Overall, results indicate the feasibility of employing this spatial filtering technique for improving elasticity image quality while preserving lesion boundaries.