Detection of suspicious lesions in dynamic contrast enhanced MRI data

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Volumn 26 I, Issue , 2004, Pages 454-457

  Twellmann, T ^a   Saalbach, A ^a   Muller C ^a   Nattkemper, T W ^a   Wismuller A ^b  

^a BIELEFELD UNIVERSITY   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

Dynamic Contrast Enhanced MRI; Multi temporal Image Analysis; Object Detection; Support Vector Machine

Indexed keywords

ALGORITHMS; DATA REDUCTION; IMAGE ANALYSIS; MAMMOGRAPHY; NEURAL NETWORKS; SET THEORY; VECTORS;

DYNAMIC CONTRAST ENHANCED MRI; MULTI-TEMPORAL IMAGE ANALYSIS; OBJECT DETECTION; SUPPORT VECTOR MACHINES (SVM);

MAGNETIC RESONANCE IMAGING;

EID: 11044228370     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. C. K. Kuhl, P. Mielcareck, S. Klaschik, C. Leutner, E. Wardelmann, J. Gieseke, and H. H. Schild, "Dynamic breast MR imaging: Are signal intensity time course data useful for differential diagnosis of enhancing lesions?," Radiology, vol. 211, no. 101, 1999.
2. B. K. Szabo, P. Aspelin, M. Kristoffersen Wiberg, and B. Bone, "Dynamic MR Imaging of the Breast - Analysis of kinetic and morphologic diagnostic criteria," Acta Radiologica, vol. 44, pp. 379-386, 2003.
3. D. Weinstein, S. Strano, P. Cohen, S. Fields, J. M. Gomori, and H. Degani, "Mapping pathophysiologic features if breast fibroadenoma by the three time point (3tp) contrast enhanced MRI method: pilot study," Radiology, vol. 210, pp. 233-240.
4. L. P. Clarke, R. P. Velthuizen, M. A. Camacho, J. J. Heine, M. Vaidyanathan, L. O. Hall, R. W. Thatcher, and M. L. Silbiger, "MRI Segmentation: Methods and Applications," Magnetic Resonance Imaging, vol. 13, no. 3, pp. 343-368, 1995.
5. A.Wismüller, O. Lange, D. Dersch, G. Leinsinger, K. Hahn, B. Ptz, and D. Auer, "Cluster analysis of biomedical image time series," Int. Journal of Computer Vision, vol. 46, no. 2, pp. 103-128, 2002.
6. R. E. A. Lucht, M. V. Knopp, and G. Brix, "Classification of signal-time curves from dynamic MR mammography by neural networks," Magnetic Resonance Imaging, vol. 19, pp. 51-57, 2001.
7. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, Wiley, 1973.
8. V. Vapnik, The nature of statistical learning theory, Springer, 1995.
9. K. P. Bennett and C. Campbell, "Support vector machines: hype or hallelujah?," SIGKDD Explor. Newsl., vol. 2, no. 2, pp. 1-13, 2000.
10. B. Schölkopf and A. Smola, Learning with Kernels, MIT Press, 2002.
11. B. Schölkopf, S. Mika., C. Burges, Knirsch, K. R. Müller, G. Rätsch, and A. J. Smola, "Input space vs. feature space in kernel-based methods," IEEE Trans. on Neural Networks, vol. 10, no. 5, pp. 1000-1017, 1999.
12. N. Otsu, "A threshold selection method from gray level histograms," IEEE Trans. Systems, Man and Cybernetics, vol. 9, no. 1, pp. 62-66, 1979.
13. J. Platt, "Advances in Kernel Methods - Support Vector Learning," chapter Fast training of support vector machines using sequential minimal optimization, pp. 185-208. MIT Press, 1999.
14. J. A. Hanley, "Receiver operating characteristic methodology: the state of the art," CRC critical reviews in diagnostic imaging, vol. 29, pp. 307-335, 1989.