An investigate of mass diagnosis in mammogram with random forest

Correct mass diagnosis in mammogram can reduce the unnecessary biopsy without increasing false negatives. In this paper, we investigated the usage of random forest classifier for the classification of masses with geometry and texture features. Before extracting features, the mass regions need to be extracted. Based on the initial contour guided by radiologist, level set segmentation is used to deform the contour and achieves the final segmentation. The proposed level set method integrated both region information and boundary information, and with a level regularization term, it can achieve accurate segmentation. Modified Hu moments were used for shape characteristics, and GLCM (Gray Level Co-occurrence Matrix) features are used for texture characteristics. Random forest, a recently proposed ensemble learning method, for the first time, is investigated for the mass classification, and is compared with SVM (Support Vector Machine). Mammography images from DDSM were used for experiment. The new method based on the level set segmentation and the features achieved a A_z value of 0.86 with SVM and 0.83 with random forest. The experimental result shows that random forest is a promising method for the diagnosis of masses.