Breast cancer is the second major cause of death by cancer in the female population and the fifth leading cause of death from cancer overall. However, it is known that breast cancer has a better prognosis and higher chances of cure if diagnosed at an earlier stage. Therefore, an early detection is extremely important and the more information is available to the physician, the greater the chances of a correct diagnosis. This paper presents a methodology for breast cancer classification using a set of novel shape descriptors in medical image processing and Support Vector Machine (SVM). These shape descriptors have been used in dental ecology as ecometrics, characteristics of organisms that reflect a species' ecology, to analyze dental shape of mammals and reconstruct past environments. The proposed method using these shape descriptors as features for breast cancer classification revealed positive results. The best result achieved was of 92.15% of accuracy, 91.40% of sensitivity and 92.90% of specificity.