Dairy Cattle Sub-clinical Uterine Disease Diagnosis Using Pattern Recognition and Image Processing Techniques

This work presents a framework for diagnosing sub-clinical endometritis, a common uterine disease in dairy cattle, based in the analysis of ultrasound images of the uterine horn. The main contribution consists in the feature extraction proposal, based on the characteristics that the expert takes into account for diagnosing, such as statistics measures, image textures, shape, custom thickness measures and histogram, among others. Given the segmentation of the different regions of the uterine horn, a fully automatic supervised classification is performed, using a model based on C-SVM. Two different datasets of ultrasound images were used, acquired and tagged by an expert. The proposed framework shows promising results, allowing to consider the development of a complete automatic procedure to measure morphological features of the uterine horn that may contribute in the diagnosis of the pathology.