Level-set based infrared image segmentation for automatic veterinary health monitoring

Modern livestock farming follows a trend to higher automation and monitoring standards. Novel systems for the health monitoring of animal like dairy cows are under development. The application of infrared thermography (IRT) for medical diagnostics was suggested long ago, but the lack of suitable technical solutions still prevents an efficient use. Within the R&D project VIONA new solutions were developed to provide veterinary IRT based diagnostic procedures. Therefor a reliable object detection and segmentation of the IR images is required. Due to the significant shape variation of the objects of interest advanced segmentation methods are necessary. The level set approach is applied to veterinary IR images for the first time. The special features of the thermal infrared spectrum requre extensive adaptations of the approach. The suggested probability based shape prior and results of the successful application on IR images of dairy cows are presented.