Abstract
Cerebral researches of Drosophila melanogaster have been prominent issues. Among them the functionalities of the glomeruli in antenna lobe of D. melanogaster draw the greatest attention. Scientists are striving to gather statistics through a constructed standard glomerular model. However, it is indeed a laborious and complicated process to perform 3D segmentation of glomeruli from various flies. If edges from 2D space are semi-automatically given and stacked up as reliable guidance in the 3D space, a source model can warp to them and be segmented with higher accuracy and less manual effort.To achieve the desire of semi-automatic edge detection on glomerular images, we developed a method inspired from the concept of ‘image analogy’ in this thesis. Users could provide a source pair, including one original image and the other the corresponding edge map, as the prior guidance. Patch-wise analogy was then applied between patches from the input image and ones from the database, which was constructed by cutting the source pair to patches with different orientations. Two kinds of feature related to gradient were adopted: one was Gaussian partial derivative, and the other was the non-maximal-suppressed edge map. Furthermore, on purpose of calculating the best weights of each feature, we formulated the analogizing process to a regression problem and solved the weights by least squares approach. We also set a threshold to keep the important edges in advance as well as improve the blind spot of the analogized error. Finally, a framework that only asked for single source pair and an adjusted threshold was formed. It performed image analogy and edge detection semi-automatically, and synthesize reliable edge maps step by step.