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Automated detection of elementary calcium release events using the A Trous wavelet transform

机译:使用Trous小波变换自动检测基本的钙释放事件

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We developed an algorithm for the automated detection and analysis of elementary Ca2+ release events ( ECRE) based on the two-dimensional nondecimated wavelet transform. The transform is computed with the "a trous'' algorithm using the cubic B-spline as the basis function and yields a multiresolution analysis of the image. This transform allows for highly efficient noise reduction while preserving signal amplitudes. ECRE detection is performed at the wavelet levels, thus using the whole spectral information contained in the image. The algorithm was tested on synthetic data at different noise levels as well as on experimental data of ECRE. The noise dependence of the statistical properties of the algorithm ( detection sensitivity and reliability) was determined from synthetic data and detection parameters were selected to optimize the detection of experimental ECRE. The wavelet-based method shows considerably higher detection sensitivity and less false-positive counts than previously employed methods. It allows a more efficient detection of elementary Ca2+ release events than conventional methods, in particular in the presence of elevated background noise levels. The subsequent analysis of the morphological parameters of ECRE is reliably reproduced by the analysis procedure that is applied to the median filtered raw data. Testing the algorithm more rigorously showed that event parameter histograms ( amplitude, rise time, full duration at half-maximum, and full width at half-maximum) were faithfully extracted from synthetic, "in-focus'' and "out-of-focus'' line scan sparks. Most importantly, ECRE obtained with laser scanning confocal microscopy of chemically skinned mammalian skeletal muscle fibers could be analyzed automatically to reproducibly establish event parameter histograms. In summary, our method provides a new valuable tool for highly reliable automated detection of ECRE in muscle but can also be adapted to other preparations.
机译:我们开发了一种基于二维非抽取小波变换的自动检测和分析基本Ca2 +释放事件(ECRE)的算法。使用三次B样条作为基本函数,通过“三重”算法计算该变换,并对该图像进行多分辨率分析,该变换可在保持信号幅度的同时高效降低噪声。小波能级,从而利用图像中包含的全部光谱信息,对该算法在不同噪声水平下的合成数据以及ECRE实验数据上进行了测试,该算法的统计特性(检测灵敏度和可靠性)对噪声的依赖性从合成数据中确定,并选择检测参数以优化实验ECRE的检测基于小波的方法显示出比以前采用的方法高得多的检测灵敏度和更少的假阳性计数,从而可以更有效地检测基本的Ca2 +释放事件与传统方法相比,尤其是在背景噪点较高的情况下自我水平。 ECRE形态参数的后续分析通过应用于中值滤波原始数据的分析程序可靠地再现。更加严格地测试算法,可以发现事件参数直方图(幅度,上升时间,最大持续时间为半最大值)和全宽度为最大半值是从合成,“聚焦”和“失焦”中真实提取的线扫描火花。最重要的是,可以自动分析化学剥皮的哺乳动物骨骼肌纤维的激光扫描共聚焦显微镜获得的ECRE,以可重复地建立事件参数直方图。总之,我们的方法为高度可靠地自动检测肌肉中的ECRE提供了一种新的有价值的工具,但也可以适用于其他准备工作。

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