@inproceedings{MeyerBaeseBarbuLobbesetal., author = {Meyer-Baese, Anke and Barbu, Adrian and Lobbes, Marc and Hoffmann, Sebastian and Burgeth, Bernhard and Kleefeld, Andreas and Meyer-Baese, Uwe}, title = {Computer-aided diagnosis of breast MRI with high accuracy optical flow estimation}, series = {Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII}, booktitle = {Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII}, editor = {Szu, Harold H. and Dai, Liyi and Zheng, Yufeng}, publisher = {SPIE}, address = {Bellingham, Wash.}, doi = {10.1117/12.2181815}, abstract = {Non-mass enhancing lesions represent a challenge for the radiological reading. They are not well-defined in both morphology (geometric shape) and kinetics (temporal enhancement) and pose a problem to lesion detection and classification. To enhance the discriminative properties of an automated radiological workflow, the correct preprocessing steps need to be taken. In an usual computer-aided diagnosis (CAD) system, motion compensation plays an important role. To this end, we employ a new high accuracy optical flow based motion compensation algorithm with robustification variants. An automated computer-aided diagnosis system evaluates the atypical behavior of these lesions, and additionally considers the impact of non-rigid motion compensation on a correct diagnosis.}, language = {en} }