Learning-based deformable registration of MR brain images

• Published in: IEEE transactions on medical imaging 2006-09, Vol.25 (9), p.1145-1157
• Main Authors: Wu, Guorong, Qi, Feihu, Shen, Dinggang
• Format: Article
• Language: English
• Subjects: Algorithms; Anatomical structure; Artificial Intelligence; Best features; best scale selection; Biomedical imaging; Biomedical measurements; Brain - anatomy & histology; Brain modeling; Computer science; consistency measurement; deformable registration; feature-based registration; hierarchical registration; Humans; Image color analysis; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image registration; Information Storage and Retrieval - methods; Learning systems; learning-based method; Magnetic resonance; Magnetic Resonance Imaging - instrumentation; Magnetic Resonance Imaging - methods; Medical simulation; Pattern Recognition, Automated - methods; Phantoms, Imaging; Reproducibility of Results; saliency measurement; Sensitivity and Specificity; Subtraction Technique
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2006.879320

This paper presents a learning-based method for deformable registration of magnetic resonance (MR) brain images. There are two novelties in the proposed registration method. First, a set of best-scale geometric features are selected for each point in the brain, in order to facilitate correspondence detection during the registration procedure. This is achieved by optimizing an energy function that requires each point to have its best-scale geometric features consistent over the corresponding points in the training samples, and at the same time distinctive from those of nearby points in the neighborhood. Second, the active points used to drive the brain registration are hierarchically selected during the registration procedure, based on their saliency and consistency measures. That is, the image points with salient and consistent features (across different individuals) are considered for the initial registration of two images, while other less salient and consistent points join the registration procedure later. By incorporating these two novel strategies into the framework of the HAMMER registration algorithm, the registration accuracy has been improved according to the results on simulated brain data, and also visible improvement is observed particularly in the cortical regions of real brain data