Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
An analysis of histogram-based thresholding algorithms
CVGIP: Graphical Models and Image Processing
Fuzzy distance transform: theory, algorithms, and applications
Computer Vision and Image Understanding
Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Combined Segmentation and Tracking of Overlapping Objects With Feedback
WOMOT '01 Proceedings of the IEEE Workshop on Multi-Object Tracking (WOMOT'01)
RAGS: region-aided geometric snake
IEEE Transactions on Image Processing
Automatic embryonic stem cells detection and counting method in fluorescence microscopy images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
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In order to understand the development of stem cells into specialized mature cells it is necessary to study the growth of cells in culture. For this purpose it is very useful to have an efficient computerized cell tracking system. In order to get reliable tracking results it is important to have good and robust segmentation of the cells. To achieve this we have implemented three levels of segmentation: based on fuzzy threshold and watershed segmentation of a fuzzy gray weighted distance transformed image; based on a fast geometric active contour model by the level set algorithm and interactively inspected and corrected on the crucial first frame. For the tracking all cells are classified into inactive, active, dividing and clustered cells. A special backtracking step is used to automatically correct for some common errors that appear in the initial forward tracking process.