Automated Multimodal Computer Aided Detection Based on a 3D-2D Image Registration
作者： T. Hopp , B. Neupane , N. V. Ruiter
DOI: 10.1007/978-3-319-41546-8_50
关键词: Combined approach 、 Artificial intelligence 、 Sensitivity (control systems) 、 Computer vision 、 Image registration 、 Computer science 、 Computer aided detection 、 Mammography 、 X ray mammography
摘要: Computer aided detection CADe of breast cancer is mainly focused on monomodal applications. We propose an automated multimodal approach, which uses patient-specific image registration MRI and X-ray mammography to estimate the spatial correspondence tissue structures. Then, based correspondence, features are extracted from both mammography. As proof principle, distinct regions interest ROI were classified into normal suspect tissue. investigated performance different classifiers, compare our combined approach against a classification with only evaluate influence error. Using information, sensitivity for detecting ROIs improved by 7i¾?% compared MRI-only detection. The error influences results: using datasets below $$10\,mm$$, increases 10i¾?% maximum 88i¾?%, while specificity remains constant. conclude that automatically combining can enhance result system.
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Shandong Wu, Susan Weinstein, Brad M. Keller, Emily F. Conant, Despina Kontos, Fully-Automated Fibroglandular Tissue Segmentation in Breast MRI Breast Imaging. pp. 244- 251 ,(2012) , 10.1007/978-3-642-31271-7_32
Hadassa Degani, Vadim Gusis, Daphna Weinstein, Scott Fields, Shalom Strano, Mapping pathophysiological features of breast tumors by MRI at high spatial resolution. Nature Medicine. ,vol. 3, pp. 780- 782 ,(1997) , 10.1038/NM0797-780
Weijie Chen, Maryellen L. Giger, Hui Li, Ulrich Bick, Gillian M. Newstead, Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magnetic Resonance in Medicine. ,vol. 58, pp. 562- 571 ,(2007) , 10.1002/MRM.21347
H.D. Cheng, Juan Shan, Wen Ju, Yanhui Guo, Ling Zhang, Automated breast cancer detection and classification using ultrasound images: A survey Pattern Recognition. ,vol. 43, pp. 299- 317 ,(2010) , 10.1016/J.PATCOG.2009.05.012
Mary M. Galloway, Texture analysis using gray level run lengths Computer Graphics and Image Processing. ,vol. 4, pp. 172- 179 ,(1975) , 10.1016/S0146-664X(75)80008-6
Angela W.C. Lee, Vijayaraghavan Rajagopal, Thiranja P. Babarenda Gamage, Anthony J. Doyle, Poul M.F. Nielsen, Martyn P. Nash, Breast lesion co-localisation between X-ray and MR images using finite element modelling. Medical Image Analysis. ,vol. 17, pp. 1256- 1264 ,(2013) , 10.1016/J.MEDIA.2013.05.011
Thomy Mertzanidou, John Hipwell, Stian Johnsen, Lianghao Han, Bjoern Eiben, Zeike Taylor, Sebastien Ourselin, Henkjan Huisman, Ritse Mann, Ulrich Bick, Nico Karssemeijer, David Hawkes, MRI to X-ray mammography intensity-based registration with simultaneous optimisation of pose and biomechanical transformation parameters. Medical Image Analysis. ,vol. 18, pp. 674- 683 ,(2014) , 10.1016/J.MEDIA.2014.03.003
Robert M. Haralick, K. Shanmugam, Its'Hak Dinstein, Textural Features for Image Classification IEEE Transactions on Systems, Man, and Cybernetics. ,vol. SMC-3, pp. 610- 621 ,(1973) , 10.1109/TSMC.1973.4309314
Yading Yuan, Maryellen L. Giger, Hui Li, Neha Bhooshan, Charlene A. Sennett, Multimodality Computer-Aided Breast Cancer Diagnosis with FFDM and DCE-MRI Academic Radiology. ,vol. 17, pp. 1158- 1167 ,(2010) , 10.1016/J.ACRA.2010.04.015
T. Hopp, M. Dietzel, P.A. Baltzer, P. Kreisel, W.A. Kaiser, H. Gemmeke, N.V. Ruiter, Automatic multimodal 2D/3D breast image registration using biomechanical FEM models and intensity-based optimization Medical Image Analysis. ,vol. 17, pp. 209- 218 ,(2013) , 10.1016/J.MEDIA.2012.10.003