Research

In this study, we propose an improved algorithm for multiple behavior quantification in laboratory mice by means of HFR video analysis. This algorithm detects when and where a mouse performs repetitive movements of its limbs at dozens of hertz in HFR videos and quantifies these behaviors by calculating the frame-to-frame difference features in four segmented regions the head, the left side, the right side, and the tail.

The algorithm can quantify multiple model behaviors independently of the position and orientation of the animal by analyzing its silhouette in a polar coordinate system, whose angular coordinate is adjusted according to the head and tail positions. Our algorithm is divided into three parts: A) Polar transform for silhouette contour extraction B) Shift- and orientation-invariant frame-to-frame difference feature calculation C) Behavior discrimination using a behavior look-up table In this study, six model behaviors were detected using frame-to-frame difference features in the four segmented regions: moving, rearing, immobility, head grooming, left-side scratching, and right-side scratching.

For verifying the proposed behavior recognition algorithm, we recorded HFR videos of three ICR mice. The lower figure shows a sequence of input images, silhouette contours, and frame-to-frame difference features in the case of left-side scratching. The detection correct ratios in the automatically determined results were above 80% for all the 3 ICR mice, compared with the manually observed results; these ratios are sufficiently high for most quantification requirements in animal testing experiments.