荧光显微图像序列中活性线粒体的跟踪算法

生物图像数据的定量分析通常都包含像素点的检测,使用荧光显微技术的活性线粒体活动图像信噪比很低,线粒体检测和跟踪困难,尤其当线粒体的运动包含了其本身的自主运动和神经元轴突带来的扰动时,更不容易获得线粒体运动曲线。本文提出了一种活性线粒体的跟踪算法,首先对线粒体图像序列进行帧间配准,使得图像中轴突的外廓对齐,其中选取边缘角点作为线粒体的特征点,再在准确对齐轴突外廓的前提下运用帧间位移矢量跟踪线粒体粒子。本文算法已经成功应用在存在神经元轴突和线粒体同时运动的动态图像序列中,整个算法流程不需要手动提取特征点,节约时间,为医学图像处理与生物技术研究提供了一种新方法和新参考。

A tracking algorithm for live mitochondria in fluorescent microscopy images

Quantitative analysis of biological image data generally involves the detection of many pixel spots. In live mitochondria video image, for which fluorescent microscopy is often used, the signal-to-noise ratio (SNR) can be extremely low, making the detection and tracking of mitochondria particle difficult. It is especially not easy to get the movement curve when the movement of the mitochondria involves its self-move and the motion caused by the neuron. An tracking algorithm for live mitochondria is proposed in this paper. First the whole image sequence is frame-to-frame registered, in which the edge corners are chosen to be the feature points. Then the mitochondria particles are tracked by frame-to-frame displacement vector. The algorithm proposed has been applied to the dynamic image sequence including neuron and mitochondria, saving time without manually picking up the feature points. It provides an new method and reference for medical image processing and biotechnological research.