Abstract
In basic research of life science, a fruit fly, Drosophila melanogaster, with the abilities of learning and memory is chosen for research to facilitate the understanding of structures and functions of the brain neural network. In the neurobiology of Drosophila, a standard brain (atlas) is necessary. The 3-D surface model of the Drosophila brain is constructed from confocal microscopy scans of individual Drosophila brains. Combining all these individual brain models and applying the model averaging procedure, we can finally generate the 3-D Drosophila brain atlas. However, before applying model averaging procedure, an issue still needs to be considered, i.e., the individual variations of each brain surface model. For example, in our brain surface models, there are some narrow and concave-shaped structures with different orientations in each individual brain model. This would cause structure errors during the model averaging procedure. In our experiments, we could see that there are some close-up structures in the averaged brain atlas, different from the original concave-shaped structures of individual brain models. To solve this problem, we proposed a semi-automatic Bezier curve based feature extraction algorithm for surface models. By using our algorithm, we can extract the features of each individual brain surface model semi-automatically. Furthermore, by applying a general averaging procedure to these feature curves, we can use the averaged feature curves as the reference for local surface warping. Experimental results show that most of the desired features of our brain models could be extracted well, and perform our algorithm before model averaging procedure leads to a more accurate brain atlas. This thesis will focus on the semi-automatic Bezier curve based feature extraction algorithm for surface models.