Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE).
Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE).
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DOI：
10.1186/1471-2105-14-s10-s8
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发表时间：
2013
影响因子：
3
通讯作者：
Ribeiro AS
中科院分区：
文献类型：
--
作者：
Chowdhury S;Kandhavelu M;Yli-Harja O;Ribeiro AS
细胞成像正成为细胞和分子生物学研究不可或缺的工具。然而，研究的大多数过程本质上是随机的，需要观察许多细胞和事件。理想情况下，从这些图像中提取信息应该依靠自动方法。在这里，我们提出了一种新的分割方法，MAMLE，用于检测密集簇内的细胞。MAMLE分两个阶段执行细胞分割。第一个依赖于国家的最先进的滤波技术，边缘检测在多分辨率与形态算子和阈值分解的自适应阈值。从这个结果中，应用校正程序，利用最大似然估计作为目标函数。同时，从初始分割中提取形态学特征，构造似然参数，得到最终分割。我们进行了经验评估，包括来自不同成像方式和不同细胞类型的样本图像。新方法在所有情况下都获得了非常高的细胞分割准确率（90%以上）。最后，将其准确性与几种现有方法进行了比较，在所有测试中，MAMLE在分割准确性方面都优于它们。
Cell imaging is becoming an indispensable tool for cell and molecular biology research. However, most processes studied are stochastic in nature, and require the observation of many cells and events. Ideally, extraction of information from these images ought to rely on automatic methods. Here, we propose a novel segmentation method, MAMLE, for detecting cells within dense clusters. MAMLE executes cell segmentation in two stages. The first relies on state of the art filtering technique, edge detection in multi-resolution with morphological operator and threshold decomposition for adaptive thresholding. From this result, a correction procedure is applied that exploits maximum likelihood estimate as an objective function. Also, it acquires morphological features from the initial segmentation for constructing the likelihood parameter, after which the final segmentation is obtained. We performed an empirical evaluation that includes sample images from different imaging modalities and diverse cell types. The new method attained very high (above 90%) cell segmentation accuracy in all cases. Finally, its accuracy was compared to several existing methods, and in all tests, MAMLE outperformed them in segmentation accuracy.