Mammogram mass classification using various geometric shape and margin features for early detection of breast cancer
Surendiran Balasubramanian, Appachi Vadivel · International Journal of Medical Engineering and Informatics · 2012
This paper focuses on an approach for characterising the mammogram masses using various geometric shape and margin features. According to BIRADS system, benign and malignant masses can be differentiated using its shape, size and density features, which is how radiologist visualise the mammograms. According to BIRADS, benign masses are round, oval, lobular in shape and malignant masses are lobular or irregular in shape. Various 17 geometrical shape and margin features are introduced to characterise the morphology of masses, as there is no single measure to differentiate various shapes. Experiments have been conducted on 1553 DDSM database mammograms and classified using CART classifier. Experimental results indicate that CART can classify masses effectively and generates simple rules, which can be easily implemented in any system using if..then..else statements. Experimental results are found to be encouraging. The results demonstrate the effectiveness of CART classifier for classifying masses as benign, malignant and normal.