Characterization of posterior acoustic features of breast masses on ultrasound images using artificial neural network
Jing Cui, Berkman Sahiner, Heang‐Ping Chan, Chintana Paramagul, Alexis V. Nees, Lubomir M. Hadjiiski, Yi‐Ta Wu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
Posterior acoustic enhancement and shadowing on ultrasound (US) images are important features used by radiologists for characterization of breast masses. We are developing new feature extraction and classification methods for computerized characterization of posterior acoustic patterns of breast masses into shadowing, no pattern, or enhancement categories. The sonographic mass was segmented using an automated active contour segmentation method. Three adjacent rectangular regions of interest (ROIs) of identical sizes were automatically defined at the same depth immediately behind the mass. Three features related to enhancement, shadowing, and no posterior pattern were designed by comparing the image intensities within these ROIs. Artificial neural network (ANN) classifiers were trained using a leave-one-case-out resampling method. Two radiologists provided posterior acoustic descriptors for each mass. Posterior acoustic patterns of masses for which both radiologists were in agreement were used as the ground truth, and the agreement of the ANN scores with the radiologists' assessment was used as the performance measure. On a data set of 339 US images containing masses, the overall agreement between the computer and the radiologists was between 86% and 87% depending on the ANN architecture. The output score of the designed ANN classifiers may be useful in computer-aided breast mass characterization and content-based image retrieval systems.