Strain estimation in elastography using scale-invariant keypoints tracking

Abstract

This paper proposes a novel strain estimator using scale-invariant keypoints tracking (SIKT) for ultrasonic elastography. This method is based on tracking stable features between the pre- and post-compression A-lines to obtain tissue displacement estimates. The proposed features, termed scaleinvariant keypoints, are independent of signal scale change according to the scale-space theory, and therefore can preserve their patterns while undergoing a substantial range of compression. The keypoints can be produced by searching for repeatedly assigned points across all possible scales constructed from the convolution with a one-parameter family of Gaussian kernels. Because of the distinctive property of the keypoints, the SIKT method could provide a reliable tracking over changing strains, an effective resistance to anamorphic noise and sonographic noise, and a significant reduction in processing time. Simulation and experimental results show that the SIKT method is able to provide better sensitivity, a larger dynamic range of the strain filter, higher resolution, and a better contrast- to-noise ratio (CNRe) than the conventional methods. Moreover, the computation time of the SIKT method is approximately 5 times that of the cross-correlation techniques.