A new algorithm of electronic cleansing for weak faecal-tagging CT colonography

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8184 LNCS, Issue , 2013, Pages 57-65

  Lu, Le ^a   Jian, Bing ^a   Wu, Dijia ^a   Wolf, Matthias ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTERIZED TOMOGRAPHY; DIAGNOSIS; LEARNING SYSTEMS; MARKOV PROCESSES; MEDICAL IMAGING; PROBABILITY DENSITY FUNCTION; SEMANTICS;

CLINICAL PRACTICES; COLONIC CANCERS; CRITICAL PROBLEMS; CT COLONOGRAPHY; ELECTRONIC CLEANSING; IMAGE APPEARANCE MODELING; MARKOV RANDOM FIELDS; REGIONS OF INTEREST;

ALGORITHMS;

EID: 84886744971     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-02267-3_8     Document Type: Conference Paper

References (12)

1. Wang, Z., Liang, Z., et al.: An Improved Electronic Colon Cleansing Method for Detection of Colonic Polyps by Virtual Colonoscopy. IEEE Trans. Biomed. Engi. 53, 1635-1646 (2006)
2. Serlie, I., Vos, F., Truyen, R., Post, F., Stoker, J., van Vliet, L.: Electronic Cleansing for Computed Tomography (CT) Colonography Using a Scale-Invariant Three-Material Model. IEEE Trans. Biomed. Engi. 57, 1306-1317 (2010)
3. Liedenbaum, M., Denters, M., Zijta, F., van Ravesteijn, V., Bipat, S., Vos, F., Dekker, E., Stoker, J.: Reducing the oral contrast dose in CT colonography: evaluation of faecal tagging quality and patient acceptance. Clin. Radiol., 30-37 (2010)
4. Lu, L., Wolf, M., Liang, J., Dundar, M., Bi, J., Salganicoff, M.: A Two-level Approach Towards Semantic Colon Segmentation: Removing Extra-colonic Findings. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 1009-1016. Springer, Heidelberg (2009)
5. Wu, T., Jian, B., Zhou, X.S.: Automated identification of thoracolumbar vertebrae using orthogonal matching pursuit. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds.) MLMI 2011. LNCS, vol. 7009, pp. 126-133. Springer, Heidelberg (2011)
6. Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. IEEE Trans. Pat. Anal. Mach. Intell. 28, 1465-1479 (2006)
7. Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Trans. Pat. Anal. Mach. Intell. 24(5), 603-619 (2002)
8. Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: ICCV, pp. 1589-1596 (2005)
9. Cover, T.M., Thomas, J.A.: Elements of information theory (1991)
10. Konishi, S., Yuille, A., Coughlan, J., Zhu, S.: Statistical edge detection: Learning and evaluating edge cues. IEEE Trans. Pat. Anal. Mach. Intell. 25, 57-74 (2003)
11. Lu, L., et al.: Hierarchical learning for tubular structure parsing in medical imaging: A study on coronary arteries using 3D CT Angiography. In: ICCV (2009)
12. Boykov, Y., Funka-Lea, G.: Graph Cuts and Efficient N-D Image Segmentation. Int. J. Comp. Vis. 70(2), 109-131 (2006)