Rat Brain Registration Using Improved Speeded Up Robust Features

Abstract

This study proposes an approach that combines edge feature extraction (EFE) with speeded up robust features (SURF) to register rat brain slices. When extracting feature points, using the traditional scale-invariant feature transform (SIFT) approach has several shortcomings, including blurry edge information and the inability to accurately extract smooth edge targets. Therefore, a feature point description method that comprises EFE and SURF is proposed to match local feature points, thereby effectively capturing edge feature points and avoiding mistaking grains and noise in images for feature points. In addition, a wavelet feature description approach is used to resolve incorrect matching caused by noise. Feature point matching for local regions is devised to solve mismatch errors that occur in highly similar regions in an image. Compared to a state-of-the-art SIFT method, the proposed method generates better results for rat brain image registration in terms of subjective visual presentation and registration quality criteria.