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A parallel genetic algorithm for cell image segmentation. (English) Zbl 1260.68429

Fourey, Sebastien (ed.) et al., IWCIA 2001. Proceedings of the 8th international workshop on combinatorial image analysis, Temple University, Philadelphia, PA, USA, August 23–24, 2001. Amsterdam: Elsevier. Electronic Notes in Theoretical Computer Science 46, 214-224 (2001).
Summary: In this paper, we propose a parallel genetic algorithm for cell image segmentation under severe noise. Our contribution aims at overcoming the drawback of the slow convenence of the traditional genetic algorithm, which was used in our previous work. A priori knowledge about cell shape is incorporated in our method. That is, an elliptical cell contour model is introduced to describe the boundary of the cell. We firstly obtain the gradient image using Canny edge detector; and then use kernel-based dynamic clustering to find out the image points that have a high probability belonging to each cell. Finally a parallel genetic algorithm is used to adjust the parameters of the cell contour model to find a best matching. The segmentation results of noisy human thyroid and small intestine cell images demonstrate that the proposed method is very successful in segmenting images of elliptically shaped cells.
For the entire collection see [Zbl 1260.68012].

MSC:

68U10 Computing methodologies for image processing
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
68W10 Parallel algorithms in computer science
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References:

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