This paper investigates a novel computational approach to thyroid tissue characterization in ultrasound images. It is based on the hypothesis that tissues in thyroid ultrasound images may be differentiated by directionality patterns. These patterns may not be always distinguishable by the human eye because of the dominant image noise. The encoding of the directional patterns in the thyroid ultrasound images is realized by means of Radon Transform features. A representative set of ultrasound images, acquired from 66 patients was constructed to perform experiments that test the validity of the initial hypothesis. Supervised classification experiments showed that the proposed approach is capable of discriminating normal and nodular thyroid tissues, whereas nodular tissues can be further characterized as of high or low malignancy risk.