Fast and Robust Symmetry Detection for Brain Images Based on Parallel Scale-Invariant Feature Transform Matching and Voting

摘要

Symmetry analysis for brain images has been considered as a promising technique for automatically extracting the pathological brain slices in conventional scanning. In this article, we present a fast and robust symmetry detection method for automatically extracting symmetry axis (fissure line) from a brain image. Unlike the existing brain symmetry detection methods which mainly rely on the intensity or edges to determine the symmetry axis, our proposed method is based on a set of scale-invariant feature transform (SIFT) features, where the symmetry axis is determined by parallel matching and voting of distinctive features within the brain image. By clustering and indexing the extracted SIFT features using a GPU KD-tree, we can match multiple pairs of features in parallel based on a novel symmetric similarity metric, which combines the relative scales, orientations, and flipped descriptors to measure the magnitude of symmetry between each pair of features. Finally, the dominant symmetry axis presented in the brain image is determined using a parallel voting algorithm by accumulating the pair-wise symmetry score in a Hough space. Our method was evaluated on both synthetic and in vivo data-sets, including both normal and pathological cases. Comparisons with state-of-the-art methods were also conducted to validate the proposed method. Experimental results demonstrated that our method achieves a real-time performance and with a higher accuracy than previous methods, yielding an average polar angle error within 0.69° and an average radius error within 0.71 mm.