A Hybrid Method for the Stitching of Drosophila Brain Images from Confocal Microscope

• Main Authors: Tzeng, Gou-Bin, 曾國斌
• Other Authors: Chen, Yung-Chang
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/74981668427991435069

碩士 === 國立清華大學 === 電機工程學系 === 97 === The research in the area of Life Science has aroused more and more attention recently. By studying on the simpler neural interactions, which are established by neurotransmitters and their transition pathways inside the Drosophila brains, the ultimate goal is to figure out the neurons’ working mechanism in the Human Brain. In these researches, lots of confocal microscope images are acquired first, and then a series of image processing operations are applied to them so that these images would become suitable to further analysis by biologists. Besides, since the resolution of acquired images from confocal microscope has its limitation, a whole brain structure may be imaged into several partial image sets, and therefore, the image acquisition process may lead to some defects in the images. In this study, we propose an image stitching system for dealing with the partial image sets mentioned above. Based on pyramid structure, we make some changes to fit this particular kind of images and then accomplish multi-resolution blending. Moreover, this system also uses sub-pixel displacement analysis such that the image stitching method and registration algorithm – the results of these two may affect each other – can be integrated. By such mechanism, we can recursively adjust stitching position (registration) and blending coefficients (stitching) until the resultant image achieves an optimum. Through experimental results, it is shown that the proposed method would provide good results for target images. These results also confirm our assumption and presupposition for this particular kind of image set. The stitched image can keep as much information of original ones as possible and also maintain a certain level of smoothness.