Self-organizing features for regularized standardization of brain images
Normal variability of anatomy is a key issue in standardization. By reducing normal variability, functional activity from multiple subjects can be overlayed to study localization, and variability outside normal ranges can be used to report abnormalities. Most of the existing global standardization methods fail to align individual anatomic structures. We propose a semi-automatic, feature-based standardization technique to complement these global methods. Benefits of our method are speed and accuracy in local alignment. The method consists of three phases: In phase one, templates are generated from the atlas structures, using Self-Organizing Maps (SOMs). The parameters of each SOM are determined using a new topology evaluation technique. In phase two, the atlas templates are reconfigured using points from individual features, to establish a one-to-one correspondence between the atlas and individual structures. During training, a regularization procedure can be optionally invoked to guarantee smoothness in areas where the discrepancy between the atlas and individual feature is high. In the final phase, difference vectors are generated using the corresponding points of the atlas and individual structure. The whole image is warped by interpolation of the difference vectors through Gaussian radial basis functions, which are determined by minimizing the membrane energy. Results are demonstrated on simulated features, as well as selected sulci in brain MRIs.