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利用固定小波变换提高火花和余烬检测能力。

Improved spark and ember detection using stationary wavelet transforms.

机构信息

Department of Physiology, Medical and Health Science Centre, University of Debrecen, Debrecen, Hungary.

出版信息

J Theor Biol. 2010 Jun 21;264(4):1279-92. doi: 10.1016/j.jtbi.2010.04.005. Epub 2010 Apr 9.

Abstract

Calcium sparks and embers are localized intracellular events of calcium release in muscle cells studied frequently by confocal microscopy using line-scan imaging. The large quantity of images and large number of events require automatic detection procedures based on signal processing methods. In the past decades these methods were based on thresholding procedures. Although, recently, wavelet transforms were also introduced, they have not become widespread. We have implemented a set of algorithms based on one- and two-dimensional versions of the à trous wavelet transform. The algorithms were used to perform spike filtering, denoising and detection procedures. Due to the dependence of the algorithms on user adjustable parameters, their effect on the efficiency of the algorithm was studied in detail. We give methods to avoid false positive detections which are the consequence of the background noise in confocal images. In order to establish the efficiency and reliability of the algorithms, various tests were performed on artificial and experimental images. Spark parameters (amplitude, full width-at-half maximum) calculated using the traditional and the wavelet methods were compared. We found that the latter method is capable of identifying more events with better accuracy on experimental images. Furthermore, we extended the wavelet-based transform from calcium sparks to long-lasting small-amplitude events as calcium embers. The method not only solved their automatic detection but enabled the identification of events with small amplitude that otherwise escaped the eye, rendering the determination of their characteristic parameters more accurate.

摘要

钙火花和余辉是肌肉细胞中钙释放的局部细胞内事件,经常通过共聚焦显微镜使用线扫描成像进行研究。大量的图像和大量的事件需要基于信号处理方法的自动检测程序。在过去的几十年中,这些方法基于阈值处理程序。尽管最近也引入了小波变换,但它们并未得到广泛应用。我们实现了一组基于一维和二维多孔小波变换的算法。这些算法用于执行尖峰过滤、去噪和检测程序。由于算法依赖于用户可调节的参数,因此详细研究了这些参数对算法效率的影响。我们提供了避免由于共聚焦图像中的背景噪声而导致假阳性检测的方法。为了确定算法的效率和可靠性,在人工和实验图像上进行了各种测试。使用传统方法和小波方法计算的火花参数(幅度、半最大值全宽)进行了比较。我们发现,后一种方法能够在实验图像上以更高的准确性识别更多的事件。此外,我们将基于小波的变换从钙火花扩展到了作为钙余辉的长时间小幅度事件。该方法不仅解决了它们的自动检测问题,还能够识别幅度较小的事件,否则这些事件会被忽略,从而使确定其特征参数更加准确。

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