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用于细胞显微注射靶向的扩散张量驱动轮廓闭合

Diffusion tensor driven contour closing for cell microinjection targeting.

作者信息

Becattini Gabriele, Mattos Leonardo S, Caldwell Darwin G

机构信息

Italian Institute of Technology, Via Morego 30, 16163 Genova, Italy.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4072-5. doi: 10.1109/IEMBS.2010.5627608.

Abstract

This article introduces a novel approach to robust automatic detection of unstained living cells in bright-field (BF) microscope images with the goal of producing a target list for an automated microinjection system. The overall image analysis process is described and includes: preprocessing, ridge enhancement, image segmentation, shape analysis and injection point definition. The developed algorithm implements a new version of anisotropic contour completion (ACC) based on the partial differential equation (PDE) for heat diffusion which improves the cell segmentation process by elongating the edges only along their tangent direction. The developed ACC algorithm is equivalent to a dilation of the binary edge image with a continuous elliptic structural element that takes into account local orientation of the contours preventing extension towards normal direction. Experiments carried out on real images of 10 to 50 microm CHO-K1 adherent cells show a remarkable reliability in the algorithm along with up to 85% success for cell detection and injection point definition.

摘要

本文介绍了一种新颖的方法,用于在明场(BF)显微镜图像中稳健自动检测未染色的活细胞,目的是为自动显微注射系统生成目标列表。描述了整个图像分析过程,包括:预处理、脊增强、图像分割、形状分析和注射点定义。所开发的算法基于热扩散的偏微分方程(PDE)实现了一种新版本的各向异性轮廓完成(ACC),通过仅沿边缘的切线方向拉长边缘来改进细胞分割过程。所开发的ACC算法等同于用连续椭圆结构元素对二值边缘图像进行膨胀,该结构元素考虑了轮廓的局部方向,防止向法线方向延伸。对10至50微米的CHO-K1贴壁细胞的真实图像进行的实验表明,该算法具有显著的可靠性,细胞检测和注射点定义的成功率高达85%。

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