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.
Algorithms ; Animals ; Image Processing, Computer-Assisted ; methods ; Microscopy, Fluorescence ; Mitochondria ; metabolism ; ultrastructure ; Neurons ; ultrastructure ; Particle Size