The growing importance of nail-fold capillaroscopy imaging as a diagnostic tool in medicine increases the need to automate this process. One of the most important markers in capillaroscopy is capillary thickness. On this basis capillaries may be divided into three separate categories:
capillaries with increased loops
. In the paper we describe the problem of capillary thickness analysis automation. First, data is extracted from a segmented capillary image. Then feature vectors are constructed. They are given as an input for capillary classification method. We applied different classifiers in the experiments. The best achieved accuracy reaches 97%, which can be considered as very high and satisfying.