A rule-based approach for robust clump splitting
Pattern Recognition
Segmentation and tracking of neural stem cell
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
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Abstract: In this paper we present a new adaptive technique for segmenting a sequence of images and tracking the moving objects contained therein. The algorithm is illustrated on a biomedical problem, namely, the tracking of a group of fibroblast (tissue) cells whose motion is induced by an external electric field, using phase contrast micrographs. Because of their proximity to one another and their motion characteristics, in addition to the nature of the images, the objects cannot be segmented accurately: they appear to merge/overlap and split. An adaptive fine segmentation technique, which can handle large objects that "appear" to have merged or split, is presented in this paper. The combined segmentation/tracking is formulated as a global optimization problem where the merging/splitting and the motion characteristics of the objects determine the final results. The motion parameters of the objects are estimated using a multiassignment algorithm (a scheme that associates segmented regions to tracks) combined with a modified version of a tracking filter (motion estimator), known as the Probabilistic Data Association Filter. Analytical expressions quantifying the accuracy of the segmentation scheme are also presented. The technique is also applicable to tracking people, ground vehicles and ballistic missiles, where the targets can be closely-spaced.