Abstract
Small Bowel Motility Assessment by means of Wireless Capsule Video Endoscopy constitutes a novel clinical methodology in which a capsule with a micro-camera attached to it is swallowed by the patient, emitting a RF signal which is recorded as a video of its trip throughout the gut. In order to overcome the main drawbacks associated with this technique -mainly related to the large amount of visualization time required-, our efforts have been focused on the development of a machine learning system, built up in sequential stages, which provides the specialists with the useful part of the video, rejecting those parts not valid for analysis. We successfully used Self Organized Maps in a general semi-supervised framework with the aim of tackling the different learning stages of our system. The analysis of the diverse types of images and the automatic detection of intestinal contractions is performed under the perspective of intestinal motility assessment in a clinical environment.
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Ali, A., Santisi, J.M., Vargo, J.: Video capsule endoscopy: A voyage beyond the end of the scope. Cleveland Clinic Journal of Medicine 71(5), 415–424 (2004)
Anzali, S., Gasteiger, S.: The use of self-organizing neural networks in drug design. Perspectives 11, 273–299 (1998)
TEC Assessments. Wireless capsule endoscopy. Technical Report 17, Blue Cross and Blue Shield Association (2003)
Van Biesen, W., Sieben, G.: Application of Kohonen neural networks fort the non-morphological distinction between glomerular and tubular renal disease. Neph. Dial Transp. 13, 59–66 (1998)
Christodoulo, C.I., Pattichis, C.S.: Unsupervised pattern recognition for the classification of EMG signals. IEEE Trans. on Biom. Eng. 46(2), 169–178 (1999)
Eliakim, R.: Wireless capsule video endoscopy: three years of experience. Wd. Journ. Gastro. 10, 1238–1239 (2004)
Fireman, Z., Glukhovsky, A.: W. capsule endoscopy. IMAJ 4, 717–719 (2002)
Fireman, Z., Mahanja, E.: Diagnosing small bowel crohn’s disease with wireless capsule endoscopy. Gut 52, 390–392 (2003)
Glass, J.O., Reddick, W.E.: Hybrid artificial neural network segmentation and classification of dynamic contrast-enhanced MR imaging (DEMRI) of osteosarcoma. Magn. Res. Im. 16(9), 1075–1083 (1998)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)
Gostout, C., Adler, D.G.: State of the art, wireless capsule endoscopy. Hospital Physician 39(5), 14–22 (2003)
Kohonen, T.: Self-Organized Maps. Springer, Heidelberg (1995)
Lux, C.L., Atellzig, A.: A neural network approach to the analysis and classification of human craniofacial growth. Grow. Devel. Asign 62(3), 95–106 (1998)
Quigley, E.M.: Gastric and small intestinal motility in health and disease. Gastroenterology Clinics of North America 25, 113–145 (1996)
Rey, J.F.: European society of gastroenterology. Guideline for video capsule endoscopy 36, 656–658 (2004)
Russ, J.C.: The Image Processing Handbook, 2nd edn. IEEE Computer Society Press, Los Alamitos (1994)
Schulmann, K., Hollerbach, S.: Feasibility and diagnostic utility of video capsule endoscopy for the detection of small bowel polyps in patients with hereditary polyposis syndromes. Am. Jour. Gastro. 100(1), 27 (2005)
Spyridonos, P., Vilariño, F., et al.: Identification of intestinal motility events of capsule endoscopy video analysis. In: Blanc-Talon, J., Philips, W., Popescu, D.C., Scheunders, P. (eds.) ACIVS 2005. LNCS, vol. 3708, pp. 531–537. Springer, Heidelberg (2005)
Vapnik, V.: The nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Vilariño, F., Kuncheva, L., et al.: ROC curves and video analysis optimization in intestinal capsule endoscopy. Pat. Recog. Let. 27(8), 875–881 (2006)
Vilariño, F., Spyridonos, P., et al.: Experiments with SVM and stratified sampling with an imbalanced problem: Detection of intestinal contractions. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 783–791. Springer, Heidelberg (2005)
Walker, A.J., Cross, S.S.: Visualisation of biomedical datasets by use of growing cell structure networks: A novel diagnostic classification technique. Lancet 354, 1518–1521 (1999)
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Vilariño, F., Spyridonos, P., Vitrià, J., Malagelada, C., Radeva, P. (2006). A Machine Learning Framework Using SOMs: Applications in the Intestinal Motility Assessment. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2006. Lecture Notes in Computer Science, vol 4225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892755_19
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DOI: https://doi.org/10.1007/11892755_19
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