- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Advances in Artificial Neural Systems
Volume 2012 (2012), Article ID 919281, 8 pages
Selection of Spatiotemporal Features in Breast MRI to Differentiate between Malignant and Benign Small Lesions Using Computer-Aided Diagnosis
1Department of Computer Science, Technical University of Munich, 8574 Garching, Germany
2Department of Scientific Computing, Florida State University, Tallahassee, FL 32306-4120, USA
3Institute for Clinical Radiology, University of Munich, 81377 Munich, Germany
4Department of Electrical and Computer Engineering, FAMU/FSU College of Engineering, Tallahassee, FL 32310-6046, USA
Received 29 February 2012; Accepted 14 May 2012
Academic Editor: Juan Manuel Gorriz Saez
Copyright © 2012 F. Steinbruecker et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- S. G. Orel, M. D. Schnall, C. M. Powell et al., “Staging of suspected breast cancer: effect of MR imaging and MR-guided biopsy,” Radiology, vol. 196, no. 1, pp. 115–122, 1995.
- C. K. Kuhl, P. Mielcareck, S. Klaschik et al., “Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions?” Radiology, vol. 211, no. 1, pp. 101–110, 1999.
- M. D. Schnall, S. Rosten, S. Englander, S. G. Orel, and L. W. Nunes, “A combined architectural and kinetic interpretation model for breast MR images,” Academic Radiology, vol. 8, no. 7, pp. 591–597, 2001.
- B. K. Szabó, P. Aspelin, M. Wiberg, and B. Bone, “Dynamic MR imaging of the breast. Analysis of kinetic and morphologic diagnostic criteria,” Acta Radiologica, vol. 44, no. 4, pp. 379–386, 2003.
- A. P. Schouten van der Velden, C. Boetes, P. Bult, and T. Wobbes, “The value of magnetic resonance imaging in diagnosis and size assessment of in situ and small invasive breast carcinoma,” American Journal of Surgery, vol. 192, no. 2, pp. 172–178, 2006.
- G. M. Grimsby, R. Gray, A. Dueck et al., “Is there concordance of invasive breast cancer pathologic tumor size with magnetic resonance imaging?” The American Journal of Surgery, vol. 198, no. 4, pp. 500–504, 2009.
- M. J. Stoutjesdijk, J. J. Fütterer, C. Boetes, L. E. Van Die, G. Jager, and J. O. Barentsz, “Variability in the description of morphologic and contrast enhancement characteristics of breast lesions on magnetic resonance imaging,” Investigative Radiology, vol. 40, no. 6, pp. 355–362, 2005.
- I. M. A. Obdeijn, C. E. Loo, A. J. Rijnsburger et al., “Assessment of false-negative cases of breast MR imaging in women with a familial or genetic predisposition,” Breast Cancer Research and Treatment, vol. 119, no. 2, pp. 399–407, 2010.
- G. D. Tourassi, R. Vargas-Voracek, D. M. Catarious, and C. E. Floyd, “Computer-assisted detection of mammographic masses: a template matching scheme based on mutual information,” Medical Physics, vol. 30, no. 8, pp. 2123–2130, 2003.
- G. D. Tourassi, B. Harrawood, S. Singh, and J. Y. Lo, “Information-theoretic CAD system in mammography: entropy-based indexing for computational efficiency and robust performance,” Medical Physics, vol. 34, no. 8, pp. 3193–3204, 2007.
- G. D. Tourassi, R. Ike, S. Singh, and B. Harrawood, “Evaluating the effect of image preprocessing on an information-theoretic CAD system in mammography,” Academic Radiology, vol. 15, no. 5, pp. 626–634, 2008.
- L. Hadjiiski, B. Sahiner, and H. Chan, “Evaluating the effect of image preprocessing on an information-theoretic cad system in mammography.,” Current Opinion in Obstetrics and Gynecology, vol. 18, no. 7, pp. 64–70, 2006.
- M. A. Kupinski and M. L. Giger, “Automated seeded lesion segmentation on digital mammograms,” IEEE Transactions on Medical Imaging, vol. 17, no. 4, pp. 510–517, 1998.
- S. Behrens, H. Laue, M. Althaus et al., “Computer assistance for MR based diagnosis of breast cancer: present and future challenges,” Computerized Medical Imaging and Graphics, vol. 31, no. 4-5, pp. 236–247, 2007.
- A. Hill, A. Mehnert, S. Crozier, and K. McMahon, “Evaluating the accuracy and impact of registration in dynamic contrast-enhanced breast MRI,” Concepts in Magnetic Resonance B, vol. 35, no. 2, pp. 106–120, 2009.
- N. Papenberg, A. Bruhn, T. Brox, S. Didas, and J. Weickert, “Highly accurate optic flow computation with theoretically justified warping,” International Journal of Computer Vision, vol. 67, no. 2, pp. 141–158, 2006.
- B. Horn and B. Schunck, Determining optical flow, 1981.
- F. Steinbruecker, A. Meyer-Baese, A. Wismueller, and T. Schlossbauer, “Application and evaluation of motion compensation technique to breast mri,” in Proceedings of the Evolutionary and Bio-Inspired Computation: Theory and Applications III, vol. 7347 of Proceedings of SPIE, pp. 73470J–73470J-8, 2009.
- D. Xu and H. Li, “Geometric moment invariants,” Pattern Recognition, vol. 41, no. 1, pp. 240–249, 2008.
- A. Mademlis, A. Axenopoulos, P. Daras, D. Tzovaras, and M. G. Strintzis, “3D content-based search based on 3D Krawtchouk moments,” in Proceedings of the 3rd International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT '06), pp. 743–749, June 2006.
- P. T. Yap, R. Paramesran, and S. H. Ong, “Image analysis by Krawtchouk moments,” IEEE Transactions on Image Processing, vol. 12, no. 11, pp. 1367–1377, 2003.
- F. Jamitzky, R. W. Stark, W. Bunk et al., “Scaling-index method as an image processing tool in scanning-probe microscopy,” Ultramicroscopy, vol. 86, no. 1-2, pp. 241–246, 2001.
- S. Theodoridis and K. Koutroumbas, Pattern Recognition, Academic Press, 1998.