Summary Reasons for performing study: Subjective neurological evaluation in horses is prone to bias. An objective method of spinal ataxia detection is not subject to these limitations and could be of use in equine practice and research. Hypothesis: Kinematic data in the walking horse can differentiate normal and spinal ataxic horses. Methods: Twelve normal and 12 spinal ataxic horses were evaluated by kinematic analysis walking on a treadmill. Each body position signal was reduced to a scalar measure of uncertainty then fuzzy clustered into normal or ataxic groups. Correct classification percentage (CCP) was then calculated using membership values of each horse in the 2 groups. Subsequently, a guided search for measure combinations with high CCP was performed. Results: Eight measures of body position resulted in CCP≥70%. Several combinations of 4–5 measures resulted in 100% CCP. All combinations with 100% CCP could be obtained with one body marker on the back measuring vertical and horizontal movement and one body marker each on the right fore‐ and hindlimb measuring vertical movement. Conclusions and potential relevance: Kinematic gait analysis using simple body marker combinations can be used objectively to detect spinal ataxia in horses.

Multi-focus image fusion is becoming increasingly prevalent, as there is a strong initiative to maximize visual information in a single image by fusing the salient data from multiple images for visualization. This allows an analyst to make decisions based on a larger amount of information in a more efficient manner because multiple images need not be cross-referenced. The contourlet transform has proven to be an effective multi-resolution transform for both denoising and image fusion through its ability to pick up the directional and anisotropic properties while being designed to decompose the discrete two-dimensional domain. Many studies have been done to develop and validate algorithms for wavelet image fusion, but the contourlet has not been as thoroughly studied. When the contourlet coefficients for the wavelet coefficients are substituted in image fusion algorithms, it is contourlet image fusion. There are a multitude of methods for fusing these coefficients together and the results demonstrate that there is an opportunity for fusing coefficients together in the contourlet domain for multi-focus images. This paper compared the algorithms with a variety of no reference image fusion metrics including information theory based, image feature based and structural similarity based assessments to select the image fusion method.

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