Nair Alka, Ramanarayanan Sriprabha, Ahlawat Shikha, Koushika Sandhya, Joshi Niranjan, Sivaprakasam Mohanasankar
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:4240-3. doi: 10.1109/EMBC.2014.6944560.
Axonal transport velocities are obtained from spatio-temporal maps called kymographs developed from time-lapse confocal microscopy movies of neurons. The kymographs of axonal transport of C.elegans worms are much noisier due to in vivo nature of imaging. Existing methodologies for velocity measurement include laborious manual delineation of axonal movement ridges on the kymographs and thereby determining particle velocities from the slopes of ridges marked. Manual kymograph analysis is not only time consuming but also prone to human errors in marking the ridges. An automated algorithm to extract all the ridges and determine the velocities without significant manual efforts is highly preferred. Not many methods are currently available for such biological studies. We present an almost automated method based on information fusion using LDA classifier, morphological image processing and spline fitting for determining axonal transport velocities. Experimental analysis of 50 kymographs shows considerable reduction of 89% in time taken with manual intervention of 10.83%. Comparitive study with the results of two of the previous literatures shows that our algorithm performs better.
轴突运输速度是从称为波形图的时空图中获得的，这些波形图是根据神经元的延时共聚焦显微镜电影生成的。由于成像的体内性质，秀丽隐杆线虫轴突运输的波形图噪声要大得多。现有的速度测量方法包括在波形图上费力地手动描绘轴突运动脊，从而根据标记脊的斜率确定粒子速度。手动波形图分析不仅耗时，而且在标记脊时容易出现人为错误。非常需要一种无需大量人工即可提取所有脊并确定速度的自动化算法。目前用于此类生物学研究的方法并不多。我们提出了一种几乎自动化的方法，该方法基于使用LDA分类器、形态图像处理和样条拟合的信息融合来确定轴突运输速度。对50个波形图的实验分析表明，在人工干预时间上，耗时显著减少了89%，人工干预占比为10.83%。与之前两篇文献的结果进行的比较研究表明，我们的算法表现更好。
