A machine learning framework using SOMs: Applications in the intestinal motility assessment

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

Volumn 4225 LNCS, Issue , 2006, Pages 188-197

  Vilariño, Fernando ^a   Spyridonos, Panagiota ^a   Vitrià, Jordi ^a   Malagelada, Carolina ^b   Radeva, Petia ^a  

^a UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

^b HOSPITAL UNIVERSITARI VALL D HEBRON   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CAMERAS; EDGE DETECTION; ENDOSCOPY; MEDICAL IMAGING; SELF ORGANIZING MAPS;

INTESTINAL CONTRACTIONS; INTESTINAL MOTILITY ASSESSMENT; LEARNING STAGES;

LEARNING SYSTEMS;

EID: 33845212526     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11892755_19     Document Type: Conference Paper

References (22)

1. A. Ali, J. M. Santisi, and J. Vargo. Video capsule endoscopy: A voyage beyond the end of the scope. Cleveland Clinic Journal of Medicine, 71(5):415-424, 2004.
2. S. Anzali and S. Gasteiger. The use of self-organizing neural networks in drug design. Perspectives. Drug Disc. Des., 11:273-299, 1998.
3. TEC Assessments. Wireless capsule endoscopy. Technical Report 17, Blue Cross and Blue Shield Association, 2003.
4. W. Van Biesen and G. Sieben. Application of Kohonen neural networks fort the non-morphological distinction between glomerular and tubular renal disease. Neph. Dial Transp., 13:59-66, 1998.
5. C. I. Christodoulo and C.S. Pattichis. Unsupervised pattern recognition for the classification of EMG signals. IEEE Trans. on Biom. Eng, 46(2):169-178, 1999.
6. R. Eliakim. Wireless capsule video endoscopy: three years of experience. Wd. Journ. Castro., 10:1238-1239, 2004.
7. Z. Fireman and A. Glukhovsky. W. capsule endoscopy. IMAJ, 4:717-719, 2002.
8. Z. Fireman and E. Mahanja. Diagnosing small bowel crohns disease with wireless capsule endoscopy. Gut, 52:390-392, 2003.
9. J. O. Glass and W.E. Reddick. Hybrid artificial neural network segmentation and classification of dynamic contrast-enhanced MR imaging (DEMRI) of osteosarcoma. Magn. Res. Im., 16(9): 1075-1083, 1998.
10. R. C. Gonzalez and R. E. Woods. Digital Image Processing. Prentice Hall. 2nd. ed., 2002.
11. C. Gostout and D. G. Adler. State of the art, wireless capsule endoscopy. Hospital Physician, 39(5): 14-22, 2003.
12. T. Kohonen. Self-Organized Maps. Springer, Heidelberg Berlin, 1995.
13. C. L. Lux and A. Atellzig. A neural network approach to the analysis and classification of human craniofacial growth. Grow. Devel. Asign, 62(3):95-106, 1998.
14. E. M. Quigley. Gastric and small intestinal motility in health and disease. Gastroenterology Clinics of North America, 25:113-145, 1996.
15. J. F Rey. European society of gastroenterology. Guideline for video capsule endoscopy. Endoscopy, 36:656-658, 2004.
16. J. C. Russ. The Image Processing Handbook. IEEE Press. 2nd. ed., 1994.
17. K. Schulmann and S. Hollerbach. Feasibility and diagnostic utility of video capsule endosoopy for the detection of small bowel polyps in patients with hereditary polyposis syndromes. Am. Jour. Gastro., 100(1):27, 2005.
18. P. Spyridonos, F. Vilariño, et al. Identification of intestinal motility events of capsule endoscopy video analysis. LNCS., 3708:531-537, 2005.
19. V. Vapnik. The nature of Statistical Learning Theory. Springer Verlag, 1995.
20. F. Vilariño, L. Kuncheva, et al. ROC curves and video analysis optimization in intestinal capsule endoscopy. Pat. Recog. Let., 27(8):875-881, 2006.
21. F. Vilarino, P. Spyridonos, et al. Experiments with SVM and stratified sampling with an imbalanced problem: Detection of intestinal contractions. LNCS., 3687(2):783-791, 2005.
22. A.J. Walkor and S.S. Cross. Visualisation of biomedical datasets by use of growing cell structure networks: A novel diagnostic classification technique. Lancet, 354:1518-1521, 1999.