Breast cancer is a curse to complete human race. It grows inside breast cells silently, and soon it spreads to the whole body; as a result, the patient dies very soon. According to recently published reports, about 12.5% women are suspected of developing invasive breast cancer during their lifetime in the United States. Mammography, an imaging technique, is used in most breast cancer detection methods. A typical mammogram has poor contrast; as a result, doctors often overlook microcalcifications while using mammograms. In this study, we introduce a computer-based image analysis methodology to examine mammograms. Our proposed methodology selects doubtful areas on images, extracts hidden attributes of the selected areas, assesses the extracted values, and helps classify the images as benign or malignant. A simulation platform is developed using popular MATLAB tool to evaluate the proposed methodology. Open source mammogram images from Digital Database for Screening Mammogram (DDSM) and Mammographic Image Analysis Society (MIAS) are used to run the simulation programs. Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) algorithms are used to analyze the extracted feature values. The proposed methodology has potential to accurately examine the suspicious regions in mammograms, because the suspicious regions are converted into equivalent digital values. Experimental results suggest that feature extraction is a promising way to analyze mammogram images to treat breast cancer. LDA and SVM methods provide 100% accuracy while classifying the images. It is observed that the geometrical features (such as radius of the selected area) have less or zero impact in detecting cancer. However, the textural features (such as standard deviation of the extracted values) have significant impact in classifying the images as malignant or benign. This work can be extended to study other types of cancer such as lung and ovarian cancer.