A spline fitting algorithm for identifying cell filaments in bright field micrographs
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Bright field cellular microscopy offers an image capturing method that is both non-invasive and simple to implement. However, the resulting micrographs pose challenges for image segmentation which are compounded when the subject cells are tightly clustered or overlapping. Filamentous cyanobacteria are a type of organism that grow as linearly arranged cells forming chain-like filaments. Existing methods for bright field cell segmentation perform poorly on micrographs of these bacteria, and are incapable of identifying the filaments. Existing filament tracking methods are rudimentary, and cannot reliably account for overlapping or parallel touching filaments. We propose a new approach for identifying filaments in bright field micrographs by combining information about both filaments and cells. This information is used by an evolutionary strategy to iteratively construct a continuous spline representation that tracks the medial line of the filaments. We demonstrate that overlapping and parallel touching filaments are handled appropriately in many difficult cases.