Can we distinguish between benign and malignant breast tumors in DCE-MRI by studying a tumor's most suspect region only?

Proceedings - IEEE Symposium on Computer-Based Medical Systems

Volumn , Issue , 2013, Pages 77-82

  Glaßer, Sylvia ^a   Niemann, Uli ^a   Preim, Bernhard ^a   Spiliopoulou, Myra ^a  

^a OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; MAGNETIC RESONANCE IMAGING;

BENIGN AND MALIGNANT TUMORS; BREAST TUMOR; BREAST TUMOR CLASSIFICATIONS; CLUSTERING RESULTS; DCE-MRI; HUMAN EXPERT; ON DYNAMICS; PARAMETER SETTING;

TUMORS;

EID: 84897050488     PISSN: 10637125     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. M. Ankerst, M. M. Breunig, H.-P. Kriegel, and J. Sander. OPTICS: Ordering Points To Identify the Clustering Structure. In Proc. of ACM SIGMOD Conf. on Management of Data, pages 49-60, 1999.
2. P. A. Baltzer, D. M. Renz, P. E. Kullnig, and et al. Application of computer-aided diagnosis (CAD) in MRmammography( MRM). Acad Radiol, 16:435-442, 2009.
3. W. Chen, M. L. Giger, U. Bick, and G. M. Newstead. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Medical Physics, 33(8):2878-87, 2006.
4. M. Daszykowski, B. Walczak, and D. L. Massart. Looking for Natural Patterns in Data. Part 1: Density Based Approach. Chemometrics and Intelligent Laboratory Systems, 56:83-92, 2001.
5. H. Degani, V. Gusis, D.Weinstein, and et al. Mapping pathophysiological features of breast tumors by MRI at high spatial resolution. Nat Med, 3:780-782, 1997.
6. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proc. of Conf. on Knowledge Discovery and Data Mining (KDD), pages 226-231, 1996.
7. R. Fusco, M. Sansone, C. Sansone, and A. Petrillo. Segmentation and classification of breast lesions using dynamic and textural features in DCE-MRI. In Proc. of Computer-Based Medical Systems (CBMS), pages 1-4, 2012.
8. S. Glaßer, U. Niemann, U. Preim, B. Preim, and M. Spiliopoulou. Classification of Benign and Malignant DCE-MRI Breast Tumors by Analyzing the Most Suspect Region. In Proc. of Bildverarbeitung fur die Medizin (BVM), page to appear, 2013.
9. S. Glaßer, U. Preim, K. Tonnies, and B. Preim. A visual analytics approach to diagnosis of breast DCE-MRI data. Computers and Graphics, 34(5):602-611, 2010.
10. G. Holmes, A. Donkin, and I. Witten. WEKA: a machine learning workbench. In Proc. of the 2nd Australian and New Zealand Conf. on Intelligent Information Systems, pages 357-361, 1994.
11. K. Kailing, H.-P. Kriegel, and P. Kroger. Density-Connected Subspace Clustering for High-Dimensional Data. In Proc. of SIAM Conference on Data Mining, pages 246-257, 2004.
12. A. Karahaliou, K. Vassiou, and N. S. e. A. Arikidis. Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis. Br J Radiol, 83(83):296-306, 2010.
13. R. Kohavi and G. John. Wrappers for feature subset selection. Artificial Intelligence, 97(1-2):273-324, 1997.
14. X. Liang, K. Ramamohanarao, H. Frazer, and Q. Yang. A lesion shape and margin characterization method in dynamic contrast enhanced magnetic resonance imaging of breast. In Proc. of Biomedical Imaging (ISBI), pages 1783-1786, 2012.
15. C. Marrocco, M. Molinara, F. Tortorella, P. Rinaldi, L. Bonomo, A. Ferrarotti, C. Aragno, and S. Schiano lo Moriello. Detection of cluster of microcalcifications based on watershed segmentation algorithm. In Proc. of Computer-Based Medical Systems (CBMS), pages 1-5, 2012.
16. K. Nie, J.-H. Chen, H. J. Yu, Y. Chu, O. Nalcioglu, and M.-Y. Su. Quantitative Analysis of Lesion Morphology and Texture Features for Diagnostic Prediction in Breast MRI. Academic Radiology, 15(12):1513-1525, 2008.
17. T. Okafuji, H. Yabuuchi, H. Soeda, Y. Matsuo, T. Kamitani, S. Sakai, M. Hatakenaka, S. Kuroki, E. Tokunaga, H. Yamamoto, and H. Honda. Circumscribed mass lesions on mammography: dynamic contrast-enhanced MR imaging to differentiate malignancy and benignancy. Magn Reson Med Sci, 7(4):195-204, 2008.
18. U. Preim, S. Glaßer, B. Preim, F. Fischbach, and J. Ricke. Computer-aided diagnosis in breast DCE-MRI-Quantification of the heterogeneity of breast lesions. European Journal of Radiology, 81(7):1532-1538, 2012.
19. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.