| Summary: | 博士 === 國立清華大學 === 電機工程學系 === 100 === Thousands of genes operate in the brain, each at a different time and space, to control complex behaviors of an animal. Recent advances in genomics, computer science, nanotechnology and bioimage informatics could be brought together to answer one of the most complex questions in biology—how does a complete brain work? A key step towards understanding the development and function of the central nervous system is to characterize the physiology of interconnected neurons. It is because of that the wiring patterns of nervous system are characterized by specific synaptic connections, and the interactions among synaptic inputs enable neurons to compute the overall consequence of various stimuli and provide the cellular basis of cognitive processes and behavior. While biologists are exploring the inter-connections among neurons by trying to draw useful information from microscopy images, scientists of engineering background can assist in this worldwide research work by developing algorithms of 3D image processing.
In order to carry out these goals, three important sub-goals should be achieved first. They are (1) high-resolution image mosaicing, (2) multiscale (level-of-detail) mesh surface reconstruction, and (3) accurate volumetric modeling. The third part will be dealt with elsewhere in the future, and this research is focused on the first two parts. That is, we focus on the way to reconstruct a high-resolution multiscale mesh surface from the to-be-combined source image volumes. We hope the obtained mesh surfaces can not only act as a 3D roadmap for neural pathways, but also can connect image volumes and the surface representation thereof.
As for mosaicing problem, due to photobleaching effect, earlier-acquired images should be given more weight than later-acquired images in the mosaicing process. We incorporate these properties into a mosaicing procedure and define a multi-resolution optimum blending parameter estimation problem that can be solved by quadratic programming with linear constraints. The perceptual quality of the resulting mosaic images is compared with that of the results derived by Burt and Adelson's algorithm and the MosaicJ algorithm. Based on the proposed optimization framework, it would be easy to extend the proposed method to other scenarios with case-dependent constraints.
Next, we develop a coarse-to-fine algorithm that can reconstruct semiregular mesh surfaces from biomedical image stacks in a multiscale fashion, and the 2D contour information can be integrated with 3D structure features at the same time. The developed method first extracts the 3D structural features via wavelet analysis, and then a registration-based subdivision procedure succeeds to evaluate the optimal position of each newly interpolated vertex. Because low-passed components belonging to the coarsest domain are extracted and isolated in advance, the obtained coarsest mesh would mix less high-frequency components than typical methods. Moreover, the proposed registration-based subdivision method guarantees that each newly interpolated vertex would have its own subdivsion parameter depending on the behaviors of neighboring vertices. It means that this strategy delivers vertices to where they are supposed to be by developing a non-uniform filter.
Finally, the proposed method can be applied to scalable/progressive transmission, and the experimental results show that it performs well even in low bit-rate circumstance.
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