The larva of wild-type zebrafish is an excellent model to study clot formation, however, the analysis of respective microscopic image sequences mainly remains manual, thus tedious and error-prone. Despite the obvious benefit of automatic segmentation algorithms for biologists, no satisfactory solution exists yet, which is mainly due to inherent problems like high signal-to-noise ratio, low contrast, and motion perturbation. In this work we propose a semi-automatic segmentation algorithm, which combines a novel measure derived from dynamic textures, a temporal prior, and an edge-based refinement. With this mixture of time, texture, and gradients, we exploit the maximum range of temporal and spatial information and thus drive a contour-based segmentation to the accurate solution. Tests on a sequence of hand-labeled microscopic images demonstrate the merit of our approach.     «

The larva of wild-type zebrafish is an excellent model to study clot formation, however, the analysis of respective microscopic image sequences mainly remains manual, thus tedious and error-prone. Despite the obvious benefit of automatic segmentation algorithms for biologists, no satisfactory solution exists yet, which is mainly due to inherent problems like high signal-to-noise ratio, low contrast, and motion perturbation. In this work we propose a semi-automatic segmentation algorithm, which c...     »