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As rapid acquisition of large collections of fluorescence microscopy cell images can be automated, large-scale subcellular localizations of GFP-tagged fusion proteins can be practically accomplished. Semi-supervised learning has the potential of using a large set of unlabeled images for the recognition of subcellular organelle patterns, but the performance still has room for improvement. This paper...
In this work we propose an image registration algorithm to automatically fit protein atomic domain models into medium-resolution three-dimensional electron microscopy reconstructions (3D-EM map). The approach employs a flexible registration algorithm whose optimizer controls the generation of stereo-chemically correct models from a given reference domain belonging to a super-family of proteins. The...
Protein subcellular locations, as an important property of proteins, are commonly learned using fluorescence microscopy. Previous work by our group has shown that automated analysis of 2D and 3D static images can recognize all major subcellular patterns in fluorescence micrographs, and that automated methods can be used to distinguish patterns that are subtly different. Since many proteins are in...
We present an algorithm for the segmentation of multicell fluorescence microscopy images. Such images abound and a segmentation algorithm robust to different experimental conditions as well as cell types is becoming a necessity. In cellular imaging, among the most often used segmentation algorithms is seeded watershed. One of its features is that it tends to oversegment, splitting the cells, as well...
In cell biology, modern imaging techniques using fluorescence microscopy allow to visualize specific nuclear structures in situ in the same cell nucleus. Hence, distances between these structures can be evaluated, in particular co-localization can be investigated. When the nucleus alters its global shape, especially if the structures are imaged sequentially, the distances are changing as well and...
The subcellular location of proteins is most often determined by visual interpretation of fluorescence microscope images. In recent years, automated systems have been developed so that the protein pattern in a single cell can be objectively and reproducibly assigned to a location category. While these systems perform very well at recognizing all major subcellular structures, some similar patterns...
Tagging and tracking protein compounds/compounds are key to a better understanding of proteomics such as protein-protein interaction and protein signaling pathway. In this paper, a generalized region tracking framework by statistical particle filter (PF) is presented for tracing the movement of protein compounds in confocal microscopy images. To effectively select the features to be tracked, a grid-based...
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