Hermite Kernels for slice interpolation in medical images

Univariate Hermite interpolation of the total degree (HTD) is an algebraically demanding interpolation method that utilizes information of the values of the signal to be interpolated at distinct support positions, as well as the values of its derivatives up to a maximum available order. In this work the interpolation kernels of the univariate HTD are derived, using several approximations of the 1st and 2nd order of discrete signal derivative. We assess the derived Hermite kernels in the task of medical image slice interpolation, against several other well established interpolation techniques. Results show that specific Hermite kernels can outperform other established interpolation methods with similar computational complexity, in terms of root mean square error (RMSE), in a number of interpolation experiments, resulting in higher accuracy interpolated images.