Micogravity, as experienced during prolonged space flight, presents a problem for space exploration. Animal models, specifically tadpoles, with altered connections of the vestibular ear allow the examination of the effects of microgravity and can be quantitatively monitored through tadpole swimming behavior. We describe an image analysis framework for performing automated quantification of tadpole swimming behavior. Speckle reducing anisotropic diffusion is used to smooth tadpole image signals by diffusing noise while retaining edges. A narrow band level set approach is used for sharp tracking of the tadpole body. The use of level set method for interface tracking provides an inherent advantage of using level set based image segmentation algorithm (active contouring). Active contour segmentation is followed by two-dimensional skeletonization, which allows the automated quantification of tadpole deflection angles, and subsequently tadpole escape (or C-start) response times. Evaluation of the image analysis methodology was performed by comparing the automated quantifications of deflection angles to manual assessments (obtained using a standard grading scheme), and produced a high correlation (r<sup>2</sup> = 0.99) indicating high reliability and accuracy of the proposed method. The methods presented form an important element of objective quantification of the escape response of the tadpole vestibular system to mechanical and biochemical manipulations, and can ultimately contribute to a better understanding of the effects of altered gravity perception on humans.