Medical image interpolation based on 3D Lanczos filtering

Abstract

Several medical imaging techniques have been employed to create visual representations of internal anatomic structures, such as organs and tissues, for clinical procedures. Diagnosis and treatment of diseases have benefited from the use of imaging modalities, such as computed tomography, magnetic resonance imaging, ultrasonography, mammography, and photon emission tomography. Interpolation is a method for generating new data points on a regular grid from a set of known data points, as result of some image transformations, such as scaling and rotation. Several filters have been proposed to implement image interpolation. In this paper, we develop and evaluate a resampling method based on the three-dimensional Lanczos kernel. Experiments are conducted on several medical images and videos to demonstrate the effectiveness of the proposed interpolation approach.