Computer-aided diagnosis (CADx) is used to help radiologists in interpretation mammograms and is usually used as a second opinion by the radiologists. Improving CADx increases the treatment options and a cure is more likely. The main objective of this research is to enhance and introduce a new method for feature extraction and selection in order to build a CADx model to discriminate between cancers, benign, and healthy parenchyma. For feature extraction, we use both human features, which are obtained by Digital Database for Screening Mammography (DDSM), and computational features. For computational feature extraction, we enhance and use two pre-existed feature extraction methods, which are the Run Difference Method (RDM) and the Spatial Gray Level Dependence Method (SGLDM), and we propose a new feature extraction method called Square Centroid Lines Gray Level Distribution Method (SCLGM). Then, we evaluate and introduce a new method for feature selection by running both of forward sequential and genetic algorithm search methods individually. Later we evaluate the results.