Reference-free brain template construction with population symmetric registration

AbstractPopulation registration has been proposed for normalizing a large group of images into a common space, which is important in many clinical and research studies, such as brain development, aging, and atlas construction. Different from pairwise registration problem that aligns the target image to the reference directly, determining the reference or the hidden common space with the least bias is important in population registration. In order to decrease this bias, a lot of work takes the arithmetic mean image as the reference. However, the arithmetic mean image is usually too smooth to guide the population registration. This work presents an efficient symmetric population registration strategy for brain template construction, which defines the symmetric population center guiding population registration. This is important because the population registration problem can be translated into a series of pairwise registration problem which is easier to optimize and implement. Another prominent merit of proposed population registration algorithm is reference-free, which eliminates the reference dependency –related bias in population registration. Based on this symmetric population registration, the brain template is constructed by approximating both the population’s intensity and gradient information. In addition, we also present a new measurement named with average bias for evaluating the unbias edness of brain template. Experiments were first carried out on four synthetic...
Source: Medical and Biological Engineering and Computing - Category: Biomedical Engineering Source Type: research