Vallmitjana A, Civera-Tregon A, Hoenicka J, Palau F, Benitez R
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:4419-4422. doi: 10.1109/EMBC.2017.8037836.
We present an automatic image processing framework to study moving intracellular structures from live cell fluorescence microscopy. The system includes the identification of static and dynamic structures from time-lapse images using data clustering as well as the identification of the trajectory of moving objects with a probabilistic tracking algorithm. The method has been successfully applied to study mitochondrial movement in neurons. The approach provides excellent performance under different experimental conditions and is robust to common sources of noise including experimental, molecular and biological fluctuations.
我们提出了一个自动图像处理框架，用于研究活细胞荧光显微镜下移动的细胞内结构。该系统包括使用数据聚类从延时图像中识别静态和动态结构，以及使用概率跟踪算法识别移动物体的轨迹。该方法已成功应用于研究神经元中的线粒体运动。该方法在不同实验条件下均具有出色的性能，并且对包括实验、分子和生物波动在内的常见噪声源具有鲁棒性。
