An Unbiased Detector of Curvilinear Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of microscope images of living cells
Pattern Analysis & Applications
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Multilevel space-time aggregation for bright field cell microscopy segmentation and tracking
Journal of Biomedical Imaging - Special issue on mathematical methods for images and surfaces
Free boundary conditions active contours with applications for vision
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
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Bright field cellular microscopy is a simple and non-invasive method for capturing cytological images. However, the resulting micrographs prove challenging for image segmentation, especially with samples that have tightly clustered or overlapping cells. Filamentous cyanobacteria grow as linearly arranged cells forming chain-like filaments that often touch and overlap. Existing bright field cell segmentation methods perform poorly with 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 segmented correctly in many difficult cases.