Automatic Detection of Laser-Induced Structures in Live Cell Fluorescent Microscopy Images Using Snakes with Geometric Constraints
| Authors | |
|---|---|
| Year of publication | 2016 |
| Type | Article in Proceedings |
| Conference | IEEE 23rd International Conference on Pattern Recognition (ICPR) |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1109/ICPR.2016.7899655 |
| Field | Use of computers, robotics and its application |
| Keywords | Biological image and signal analysis; Biologically motivated vision; Segmentation features and descriptors |
| Description | The existence of reliable evaluation datasets for cell image registration algorithms is crucial for quantitative comparison of registration approaches. A new technique for creating real live cell image sequences for this purpose was introduced recently. These datasets contain stable structures bleached by argon laser in the cell nucleus. In this work, we propose an approach for automatic detection of laser-induced linear structures in live cell fluorescent microscopy images. Compared to a previous linear laser-induced structure detection approach, our method employs an active contours model with a Hessian-based image energy term for linear structures enhancement and geometric energy term controlling the geometric relations between the structures. It uses position adaptive tension parameter values to adjust the snakes behavior in problematic regions (end points and intersection points) and a temporal consistent scheme where the results from the previous frame are used as an initial approximation for the current frame. Our approach was successfully applied to real live cell microscopy image sequences and an experimental comparison with an existing laser-induced structures detection method based on minimal paths has been performed. |
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