Learning disease severity for capsule endoscopy images

Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Volumn , Issue , 2009, Pages 1314-1317

  Kumar, R ^a   Rajan, P ^a   Bejakovic, S ^a   Seshamani, S ^a   Mullin, G ^b   Dassopoulos, T ^c   Hager, G ^d  

^a Johns Hopkins University   (United States)

^b JOHNS HOPKINS HOSPITAL   (United States)

^c WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d JOHNS HOPKINS UNIVERSITY   (United States)

Author keywords

Capsule endoscopy; Disease severity; Ordinal regression; Statistical classification

Indexed keywords

ACCURACY RATE; CAPSULE ENDOSCOPY; CAPSULE ENDOSCOPY IMAGE; CLINICAL METHODS; CROHN'S DISEASE; DATA SETS; DISEASE SEVERITY; EDGE FEATURES; ORDINAL REGRESSION; PAIR-WISE COMPARISON; RANKING FUNCTIONS; REGIONS OF INTEREST; STATISTICAL CLASSIFICATION; STATISTICAL CLASSIFIER; SUBJECTIVE EVALUATIONS; WIRELESS CAPSULE ENDOSCOPY;

ENDOSCOPY;

IMAGE PROCESSING;

EID: 70449449418     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2009.5193306     Document Type: Conference Paper

References (13)

1. G. Iddan, G. Meron, A. Glukhovsky, and P. Swain, "Wireless capsule endoscopy," Nature, vol. 405, no. 6785, pp. 417, 2000.
2. B.S. Lewis, "Expanding role of capsule endoscopy in inflammatory bowel disease," World J Gastroenterol, vol. 14, no. 26, pp. 4137-4141, 2008.
3. P. Voila and M. Jones, "Robust real-time face diction [J]," International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.
4. A. Opelt, A. Pinz, M. Fussenegger, and P. Auer, "Generic Object Recognition with Boosting," IEEE PAMI, pp. 416-431, 2006.
5. R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman, "Learning object categories from google's image search," in Proc. ICCV, 2005, pp. 1816-1823.
6. J. Furnkranz and E. Hullermeier, "Pairwise Preference Learning and Ranking," Lec. Notes in Comp. Sc., pp. 145-156, 2003.
7. R. Herbrich, T. Graepel, and K. Obermayer, Regression Models for Ordinal Data: A Machine Learning Approach, Technische Universität Berlin, 1999.
8. S. Tong and E. Chang, "Support vector machine active learning for image retrieval," in Proc. of 9th ACM Int. conf. on Multimedia. ACM New York, NY, USA, 2001, pp. 107-118.
9. D.G. Lowe, "Object recognition from local scale-invariant features," in Proc. ICCV. Kerkyra, Greece, 1999, vol. 2, pp. 1150-1157.
10. Y. Liu, D. Zhang, G. Lu, and W.Y. Ma, "A survey of contentbased image retrieval with high-level semantics," Pattern Recognition, vol. 40, no. 1, pp. 262-282, 2007.
11. M. Coimbra, P. Campos, and JPS Cunha, "Topographic Segmentation and Transit Time Estimation for Endoscopic Capsule Exams," in Proc. ICASSP, 2006, vol. 2.
12. Jeongkyu Lee, JungHwan Oh, Subodh Kumar Shah, Xiaohui Yuan, and Shou Jiang Tang, "Automatic classification of digestive organs in wireless capsule endoscopy videos," in SAC07, 2007.
13. Srdan Bejakovic, Rajesh Kumar, Themistocles Dassopoulos, Gerard Mullin, and Gregory Hager, "Analysis of crohns disease lesions in capsule endoscopy images," in IEEE ICRA, 2009(accepted).