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首页> 外文会议>International Conference on Biomedical Engineering and Informatics >Microscopic Cell Detection Based on Multiple Cell Image Segmentations and Fusion Algorithms
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Microscopic Cell Detection Based on Multiple Cell Image Segmentations and Fusion Algorithms

机译：基于多个细胞图像分割和融合算法的微观电池检测

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Automatic cell segmentation in phase contrast microscopy images play a very important role in the study the behavior of lymphocytes, such as cell motility, cell deformation, and cell population dynamics etc. In this paper, we have developed a set of algorithms for the microscopy image cell segmentation, in which three pairs of edge detection (Sobel, Prewitt and Laplace) based cell segmentation algorithms are developed in parallel to increase the probability of cell detection. Then, an hierarchical model is proposed and used in decision fusion that combine the three pair of detection results to increase the probability of final cell detection. After that, a false removal algorithm is proposed to remove false detections that may occur in the fusion process. The distance and watershed transforms have also been used to separate the connected cells. Experimental results have proved that these algorithms are pretty robust to variable microscopy image data, and variable cell densities, and with the proposed fusion and false removal algorithms, the cell detection rate has increased significantly to above 97% with the false detection rate about 7%.
机译：相衬显微镜图像自动分割细胞发挥在研究中的淋巴细胞，如细胞运动，细胞变形和细胞种群动态等，本文的行为非常重要的作用，我们已经制定了一套算法的显微图像细胞分割，其中3双边缘检测（索贝尔，蒲瑞维特和拉普拉斯）基于细胞的分割算法是并行开发的，以增加小区检测的概率。然后，分层模型，提出并在决策融合相结合的三对检测结果的，以增加最终细胞检测的概率使用。此后，假去除算法以去除在融合过程中可能发生的错误检测。的距离和流域变换也已用于分离所连接的细胞。实验结果证明，这些算法是相当坚固以可变显微镜图像数据，和可变的细胞密度，并与所提出的融合和假去除算法，小区检测率显著与误检率约为7％提高到97％以上。

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