Strain measurement during tissue deformation is crucial to elucidate relationships between mechanical loading and functional changes in biological tissues. When combined with specified loading conditions, assessment of strain fields can be used to craft models that accurately represent the mechanical behavior of soft tissue. Inhomogeneities in strain fields may be indicative of normal or pathological inhomogeneities in mechanical properties. In this study, we present the validation of a modified Demons registration algorithm for non-contact, marker-less strain measurement of tissue undergoing uniaxial loading. We validate the algorithm on a synthetic dataset composed of artificial deformation fields applied to a speckle image, as well as images of aortic sections of varying perceptual quality. Initial results indicate that Demons outperforms recent Optical Flow and Digital Image Correlation methods in terms of accuracy and robustness to low image quality, with similar runtimes. Demons achieves at least 8% lower maximal deviation from ground truth on 50% biaxial and shear strain applied to aortic images. To illustrate utility, we quantified strain fields of multiple human aortic specimens undergoing uniaxial tensile testing, noting the formation of strain concentrations in areas of rupture. The modified Demons algorithm captured a large range of strains (up to 50%) and provided spatially resolved strain fields that could be useful in the assessment of soft tissue pathologies.