In this paper we analyse the performance of various texture analysis methods for the purpose of breast mass detection. We considered well-known methods such as local binary patterns, histogram of oriented gradients, cooccurrence matrix features and Gabor filters. Moreover, we propose the use of local directional number patterns as a new feature extraction method for breast mass detection. For each method, a Support Vector Machine is trained on the extracted features to predict the class (mass/normal) of unknown instances. In order to improve the mass detection capability of each individual method we used classifier majority voting and feature combination techniques. Some experiments were performed on the images obtained from a public breast cancer database, achieving promising levels of sensitivity and specificity.