中文摘要：

在大多数显微镜下面，荧光灯从生物结构射出的信号，将作为小 puncta 出现例如从单个分子的泡，以及信号，它贡献象 Gaussian 一样分布。这些点的精确分割将根本上影响我们特定的生物进步的解释。因为在图象的复杂背景，许多算法没能有趣的信号识别所有；要求让算法处理大数据集的时间的巨大的数量能也减少他们的实用程序。这里，我们基于机器学习算法 AdaBoost 介绍一个优秀柔韧的察觉方法，它在大多数状况下面超过基于阀值的分割，小浪，和食物及药品管理局。我们也提供这个算法的 GPU/multi-core 中央处理器实现；这实现加速算法近似 10 褶层和 7 褶层加速与单个中央处理器实现相比。时间的大减小应该在处理大数据集使这个方法成为一个有希望的候选人。而且，我们表明我们的算法的使用在上真荧光灯显微图，和用机器制造学习底察觉方法的结果表演超过四个另外的以前报导的方法。

英文摘要：

Under most microscopes, the fluorescent sig- nals emitted from biological structures, such as vesicles, as well as the signals from single molecules will appear as small puncta, which contribute to a Gaussian-like distri- bution. Accurate segmentation of these spots will funda- mentally affect our interpretation of a specific biological progress. Because of the complicated backgrounds in images, many algorithms fail to identify all of the inter- esting signals; the tremendous amount of time required for algorithms to process large datasets can also decrease their utility. Here, we introduce an excellent robust detection method based on the machine learning algorithm Ada- Boost, which outperforms threshold-based segmentation, wavelets, and FDA under most situations. We also provide a GPU/multi-core CPU implementation of this algorithm; this implementation accelerates the algorithm approxi- mately 10- and 7-fold acceleration compared with a single CPU implementation. The great reduction of time should make this method a promising candidate in the processing of large datasets. Furthermore, we demonstrate the use of our algorithm on true fluorescent micrographs, and the results show that machine learning-based detection meth- ods outperform the four other previously reported methods.