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Novel morphological features for non-mass-like breast lesion classification on DCE-MRI

: Razavi, M.; Wang, L.; Tan, T.; Karssemeijer, N.; Linsen, L.; Frese, U.; Hahn, H.K.; Zachmann, G.


Wang, L.:
Machine learning in medical imaging. 7th International Workshop, MLMI 2016 : Held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, proceedings
Cham: Springer International Publishing, 2016 (Lecture Notes in Computer Science 10019)
ISBN: 978-3-319-47156-3 (Print)
ISBN: 978-3-319-47157-0 (Online)
International Workshop on Machine Learning in Medical Imaging (MILMI) <7, 2016, Athens>
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) <19, 2016, Athens>
Fraunhofer MEVIS ()

For both visual analysis and computer assisted diagnosis systems in breast MRI reading, the delineation and diagnosis of ductal carcinoma in situ (DCIS) is among the most challenging tasks. Recent studies show that kinetic features derived from dynamic contrast enhanced MRI (DCE-MRI) are less effective in discriminating malignant non-masses against benign ones due to their similar kinetic characteristics. Adding shape descriptors can improve the differentiation accuracy. In this work, we propose a set of novel morphological features using the sphere packing technique, aiming to discriminate non-masses based on their shapes. The feature extraction, selection and the classification modules are integrated into a computer-aided diagnosis (CAD) system. The evaluation was performed on a data set of 106 non-masses extracted from 86 patients, which achieved an accuracy of 90.56%, precision of 90.3%, and area under the receiver operating characteristic (ROC) curve (AUC) of 0.94 for the differentiation of benign and malignant types.