A hybrid approach for brain image registration with local constraints

作者：Hsu Wei Yen

来源：Integrated Computer-Aided Engineering, 2017, 24(1): 73-85.

DOI：10.3233/ICA-160533

• Wavelet-based edge correlation
• speeded up robust features (SURF)
• local constraints
• image registration

In this study, a novel approach, which is the combination of wavelet-based edge correlation (WEC), speeded up robust features (SURF) feature descriptors and feature point matching considering local constraints, is proposed to register the tissue slices of brains. First, WEC feature extraction is proposed to effectively extract significant and true feature points located at the contours and vessels of tissue slices, which can greatly benefit the subsequent feature point matching. The extracted feature points are then constructed feature descriptors by means of SURF. Next, when the matching of feature points is performed, the local constraints are considered, which can considerably improve the matching accuracy. Finally, image registration is achieved after the transform is calculated from the correct image pairs. Compared to original SIFT, original SURF, improved SURF, and four state-of-the-art approaches, the proposed method obtains more accurate and faster results for brain image registration in terms of subjective visual presentation and several registration accuracy criteria.

• 出版日期2017