Database-guided breast tumor detection and segmentation in 2D ultrasound images

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 7624, Issue , 2010, Pages

  Zhang, Jingdan ^a   Zhou, Shaohua K ^a   Brunke, Shelby ^a   Lowery, Carol ^a   Comaniciu, Dorin ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

breast tumor detection and segmentation; database guided methods; discriminative graph cut

Indexed keywords

COMPENSATION (PERSONNEL); COMPUTER AIDED DIAGNOSIS; DATABASE SYSTEMS; DIAGNOSIS; GRAPHIC METHODS; IMAGE SEGMENTATION; TUMORS; ULTRASONIC APPLICATIONS; WAGES;

AUTOMATIC SYSTEMS; BREAST TUMOR DETECTION; COMPATIBILITY FUNCTION; EXPERT ANNOTATIONS; GRAPH CUT; TUMOR DETECTION; TUMOR SEGMENTATION; ULTRASOUND IMAGES;

MEDICAL IMAGING;

EID: 84956858671     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.844558     Document Type: Conference Paper

References (21)

1. Bosch, A., Kessels, A., Beets, G., Vranken, K., Borstlap, A., von Meyenfeldt, M., van Engelshoven, J., "Interexamination variation of whole breast ultrasound," British Journal of Radiology 76, 328-331 (2003).
2. Shankar, P., Reid, J., Ortega, H., Piccoli, C., Goldberg, B., "Use of non-rayleigh statistics for identification of tumors in ultrasonic B-scans of the breast," IEEE Trans. Medical Imaging 12(4), 687-692 (1993).
3. Stavros, A., Thickman, D., Rapp, C., Dennis, M., Parker, S., Sisney, G., "Solid breast nodules: Use of sonography to distinguish between benign and malignant lesions," Radiology 196, 122-134 (1995).
4. Garra, B., Krasner, B., Horii, S., Ascher, S., Mun, S., Zeman, R., "Improving the distinction between benign and malignant breast lesions: The value of sonographic texture analysis," J. Ultrasound Imaging 15, 267-285 (1993).
5. Chen, C., Chou, Y., Han, K., Hung, G., Tiu, C., Chiou, H., Chiou, S., "Breast lesions on sonograms: Computer-aided diagnosis with nearly setting-independent features and artificial neural networks," Radiology 226, 504-514 (2003).
6. Aleman, M., Aleman, P., Alvarez, L., Esteban, M., Fuentes, R., Montesdeoca, J., "Computerized ultrasound characterization of breast tumors," Computer Assisted Radiology and Surgery (Elsevier International Congress) 1281, 1063-1068 (2005).
7. Huang, Y., Chen, D., "Automatic contouring for breast tumors in 2-D sonography," in [Proc. IEEE Engineering in Medicine and Biology 27th Annual Conference], (2005).
8. Jolly, M., Grady, L., "3D general segmentation in CT," in [Proc. IEEE Int'l Sym. Biomedical Imaging], (2008).
9. Grady, L., "Random walks for image segmentation," IEEE Trans. Pattern Anal. Machine Intell. 28(11), 1768-1783 (2006).
10. Boykov, Y., Jolly, M., "Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images," in [Proc. Int'l Conf. Computer Vision], (2001).
11. Georgescu, B., Zhou, X. S., Comaniciu, D., Gupta, A., "Database-guided segmentation of anatomical structures with complex appearance," in [Proc. IEEE Conf. Computer Vision and Pattern Recognition], (2005).
12. Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D., "Fast automatic heart chamber segmentation from 3D CT data using marginal space learning and steerable features," in [Proc. Int'l Conf. Computer Vision], (2007).
13. Wels, M., Carneiro, G., Aplas, A., Huber, M., Hornegger, J., Comaniciu, D., "A discriminative modelconstrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3D MRI," in [Proc. Int'l Conf. Medical Image Computing and Computer Assisted Intervention], (2008).
14. Zhang, J., Zhou, S., McMillan, L., Comaniciu, D., "Joint real-time object detection and pose estimation using probabilistic boosting network," in [Proc. IEEE Conf. Computer Vision and Pattern Recognition], (2007).
15. Zhang, J., Zhou, S., Comaniciu, D., McMillan, L., "Discriminative learning for deformable shape segmentation: A comparative study," in [Proc. European Conf. Computer Vision], (2008).
16. Tu, Z., "Probabilistic boosting-tree: Learning discriminative models for classification, recognition, clustering," in [Proc. Int'l Conf. Computer Vision], (2005).
17. Freund, Y., Schapire, R., "A decision-theoretic generalization of online leaning and an application to boosting," J. Computer and System Sciences 55(1), 119-139 (1997).
18. Viola, P., Jones, M., "Rapid object detection using a boosted cascade of simple features," in [Proc. IEEE Conf. Computer Vision and Pattern Recognition], (2001).
19. Lafferty, J., Mccallum, A., Pereira, F., "Conditional random fields: Probabilistic models for segmenting and labeling sequence data," in [Proc. International Conference on Machine Learning], (2001).
20. Kumar, S., Hebert, M., "Discriminative fields for modeling spatial dependencies in natural images," in [Advances in Neural Information Processing Systems], (2003).
21. Lee, C., Greiner, R., Zaäne, O., "Efficient spatial classification using decoupled conditional random fields," in [Proc. Knowledge Discovery in Databases], (2006).