Abstract
Understanding how people learn and memorize is one of the goal for brain research. In order to simplify research, the main structures and function of Drosophila brain are being intensively studied, because it has been discovered that several brain controlling genes are very similar to human’s. In order to study the structures and function of Drosophila brain, a volumetric registration process is required to match two volume data of brains. Before volumetric registration is applied, we need to specify features to be aligned during the registration, and then deform the source brain volume into the target one according to these pre-specified features. Therefore, we need to find these feature pairs in 3D volume data of both brains. In the 3D space, it’s difficult to find feature points within the Drosophila brain visually, because we can’t see through the brain volume or other important organs inside. So, we only find feature pairs in each slice respectively. In this thesis, we develop an automatic feature point detection for Drosophila brains to solve the problem that it’s hard to find corresponding landmarks manually. In our algorithm, first we find feature points and match them globally and locally. Then, we register two different brains with thin-plate spline(TPS). The result will be very similar to target brain, containing the internal organs and characteristics.