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首页> 外文期刊>Image Processing, IEEE Transactions on >A Statistical Pixel Intensity Model for Segmentation of Confocal Laser Scanning Microscopy Images
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A Statistical Pixel Intensity Model for Segmentation of Confocal Laser Scanning Microscopy Images

机译：用于共聚焦激光扫描显微镜图像分割的统计像素强度模型

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摘要

Confocal laser scanning microscopy (CLSM) has been widely used in the life sciences for the characterization of cell processes because it allows the recording of the distribution of fluorescence-tagged macromolecules on a section of the living cell. It is in fact the cornerstone of many molecular transport and interaction quantification techniques where the identification of regions of interest through image segmentation is usually a required step. In many situations, because of the complexity of the recorded cellular structures or because of the amounts of data involved, image segmentation either is too difficult or inefficient to be done by hand and automated segmentation procedures have to be considered. Given the nature of CLSM images, statistical segmentation methodologies appear as natural candidates. In this work we propose a model to be used for statistical unsupervised CLSM image segmentation. The model is derived from the CLSM image formation mechanics and its performance is compared to the existing alternatives. Results show that it provides a much better description of the data on classes characterized by their mean intensity, making it suitable not only for segmentation methodologies with known number of classes but also for use with schemes aiming at the estimation of the number of classes through the application of cluster selection criteria.
机译：共聚焦激光扫描显微镜（CLSM）已在生命科学中广泛用于表征细胞过程，因为它可以记录荧光标记的大分子在活细胞的一部分上的分布。实际上，这是许多分子运输和相互作用定量技术的基石，其中通常需要通过图像分割来识别感兴趣区域。在许多情况下，由于所记录的细胞结构的复杂性或所涉及的数据量，图像分割太困难或效率低而无法手工完成，必须考虑使用自动分割程序。考虑到CLSM图像的性质，统计分割方法似乎是自然的候选方法。在这项工作中，我们提出了一种用于统计无监督CLSM图像分割的模型。该模型源自CLSM图像形成机制，并将其性能与现有替代方案进行了比较。结果表明，该方法可以更好地描述以平均强度为特征的类别数据，不仅适用于已知类别数的分割方法，而且适用于旨在通过分类估计数的方案集群选择标准的应用。

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