Medical Image Registration Algorithm Based on Compressive Sensing and Scale-Invariant Feature Transform

Abstract

A registration algorithm based on compressive sensing theory and SIFT(Scale-Invariant Feature Transform) is proposed. By the sparse feature representation methods, the feature vector of SIFT is extracted and the high-dimensional gradient derivative is decreased to low-dimensional sparse feature vector. Accordingly, Euclidean distance is introduced to compute the similarity and dissimilarity between feature vectors used for image registration and BBF(Best-Bin-First) data structure is used to avoid exhaustion. The experimental results show that the proposed algorithm has better performance than the traditional SIFT algorithm. Comparing with the current modified SIFT algorithms, the real-time performance of the proposed algorithm is improved obviously.