Abstract:
The fruit fly, Drosophila melanogaster, is a well established model
organism used to study the mechanisms of both learning and
memory in vivo. This paper presents video analysis algorithms that
generate data that may be used to categorize fly behaviors. The algorithms
aim to replace and improve a labor-intensive, subjective evaluation process
with one that is automated, consistent and reproducible; thus allowing for
robust, high-throughput analysis of large quantities of video data. The
method includes tracking the flies, computing geometric measures,
constructing feature vectors, and grouping the specimens using clustering
techniques. We also generated a Computed Courtship Index (CCI), a
computational equivalent of the existing Courtship Index (CI). The results
demonstrate that our automated analysis provides a numerical scoring of fly
behavior that is similar to the scoring produced by human observers.
They also show that we are able to automatically differentiate between
normal and defective flies via analysis of their videotaped movements.
