Contour model guided image warping for medical image interpolation

Abstract

An interpolation method using contours of organs as the control parameters is proposed to recover the intensity information in the physical gaps of serial cross-sectional images. In the authors' method, contour models are used for generating the control lines required for the image warping algorithm. Contour information derived from this contour-model-based segmentation process is processed and used as the control parameters to warp the corresponding regions in both input images into compatible shapes. In this way, the reliability of establishing the correspondence among different segments of the same organs is improved and the intensity information for the interpolated intermediate slices can be derived more faithfully. In comparison with the existing intensity interpolation algorithms that only search for corresponding points in a small physical neighborhood, this method provides more meaningful correspondence relationships by warping regions in images into similar shapes before resampling to account for significant shape differences. Experimental results show that this method generates more close to realistic and less blurred interpolated images especially when the shaped difference of corresponding contours is significant.