Cell image segmentation with kernel-based dynamic clustering and an ellipsoidal cell shape model.

F Yang; T Jiang

doi:10.1006/jbin.2001.1009

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Cell image segmentation with kernel-based dynamic clustering and an ellipsoidal cell shape model.

F Yang·T Jiang

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Abstract

In this paper, we propose a novel approach to cell image segmentation under severe noise conditions by combining kernel-based dynamic clustering and a genetic algorithm. Our method incorporates a priori knowledge about cell shape. That is, an elliptical cell contour model is introduced to describe the boundary of the cell. Our method consists of the following components: (1) obtain the gradient image; (2) use the gradient image to obtain points which possibly belong to cell boundaries; (3) adjust the parameters of the elliptical cell boundary model to match the cell contour using a genetic algorithm. The method is tested on images of noisy human thyroid and small intestine cells.

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Algorithms Cell Size Cluster Analysis Computational Biology Humans Image Processing, Computer-Assisted Models, Biological Models, Genetic Pathology, Clinical Journal Article Research Support, Non-U.S. Gov't

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F Yang, T Jiang. 2001. Cell image segmentation with kernel-based dynamic clustering and an ellipsoidal cell shape model.. https://doi.org/10.1006/jbin.2001.1009

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