Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
Cells segmentation from 3-D confocal images of early zebrafish embryogenesis
IEEE Transactions on Image Processing
Moving object segmentation using the flux tensor for biological video microscopy
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A novel coarse-to-fine adaptation segmentation approach for cellular image analysis
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
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We describe a new technique to automatically segment and track the cell images of a breast cancer cell line in order to study cell migration and metastasis. Within each image observable cell characteristics vary widely, ranging from very bright completely bounded cells to barely visible cells with little to no apparent boundaries. A set of different segmentation algorithms are used in series to segment each cell type. Cell segmentation and cell tracking are done simultaneously, and no user selected parameters are needed. A new method for background subtraction is described and a new method of selective dilation is used to segment the barely visible cells. We show results for initial cell growth.