Fast Registration Method for Large-Field-Of-View Nailfold Video Images Based on Improved Projection Analysis.

Abstract

In nailfold video recordings, the micro-shaking of the hand is amplified and interferes with physician observations and parameter measurement. We developed a fast and accurate registration method for large-field-of-view nailfold video images. Nailfold videos are first represented in the YCrCb color space, with the Cb spatial component replacing the original grayscale image to reduce sensitivity to illumination. The projection variance of each row/column is employed to improve registration accuracy and processing speed. The method was compared with Origin GrayDrop, feature point matching, unsupervised learning, and Adobe Premiere Pro in terms of the peak signal-to-noise ratio, structural similarity index, and mean squared error. The peak signal-to-noise ratio and structural similarity index are enhanced, and the mean squared error is reduced compared to the original projection method. Moreover, the proposed method is faster than the comparison methods and provides the best combination of registration accuracy and fast processing for nailfold video image registration.