An effective breast mass diagnosis system using Zernike moments

In this paper, a novel CADx system has been proposed for the diagnosis of masses in mammography images. The objective is intensifying the performance of CADx algorithms as well as reducing the false positive rate by utilizing Zernike moments as descriptors of shape and margin characteristics. The input ROI is segmented manually by expert radiologists. Then, it is subjected to some preprocessing stages such as histogram equalization, translation, and NRL scaling. The outcome of preprocessing stage is two processed images containing co-scaled translated masses. Besides, one of these images represents the shape characteristics of the mass, while the other describes the margin characteristics. Two groups of Zernike moments have been extracted from the preprocessed images and proceeded to the feature selection stage. Each group includes 32 moments with different orders and iterations. Considering the performance of the overall CADx system, the most effective 32 moments have been chosen and applied to a multi-layer Perceptron classifier. The ROC plot and the performance of overall CADx system are analyzed for each group of features. The designed systems yield Az = 0.976 and 0.975 which represent fair sensitivity and fair specificity, respectively. The best achieved FPR is 5.5%.