Publication details

Automatic detection of laser-induced structures in live cell fluorescent microscopy images using snakes with geometric constraints



Year of publication 2017
Type Article in Proceedings
Conference 23rd International Conference on Pattern Recognition, ICPR 2016
MU Faculty or unit

Faculty of Informatics

Keywords Argon lasers; Cells; Cytology; Fluorescence; Geometry; Image registration; Image segmentation; Matrix algebra
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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