Optimization and Engineering

, Volume 10, Issue 2, pp 289–299 | Cite as

Obscure bleeding detection in endoscopy images using support vector machines



Wireless capsule endoscopy (WCE) is a recently established imaging technology that requires no wired device intrusion and can be used to examine the entire small intestine non-invasively. Determining bleeding signs out of over 55,000 WCE images is a tedious and expensive job by human reviewing. Our goal is to develop an automatic obscure bleeding detection method by employing image color features and support vector machine (SVM) classifier. The bleeding lesion detection problem is a binary classification problem. We use SVMs for this problem and a new feature selection procedure is proposed. Our experiments show that SVM can be very efficient in processing unseen instances and may yield very high accuracy rate, in particular with our new proposed feature selection. More specifically, for this bleeding detection problem, training an SVM with 640 samples can be completed in as little as 0.01  second on a Dell workstation with dual Xeon CPUs, and classifying an image using the trained SVM can be done in as little as 0.03 milliseconds. The accuracy for both sensitivity and specificity can be over 99%.


Image classification Support vector machine Feature selection 


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  1. Adler DG, Gostout CJ (2003) Wireless capsule endoscopy. Hosp Physician 14–22 Google Scholar
  2. Boser BE, Guyon IM, Vapnik V (1992) A training algorithm for optimal margin classifiers. In: Fifth annual workshop on computational learning theory. Assoc Comput Mach, New York Google Scholar
  3. Chang CC, Lin CJ (2001) LIBSVM: A Library for Support Vector Machines. (software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm)
  4. Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273–297 MATHGoogle Scholar
  5. Cristianini N, Shawe-Taylor J (2000) Support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge Google Scholar
  6. Dumais S, Platt J, Heckerman D, Sahami M (1998) Inductive learning algorithms and representations for text categorization. In: Proceedings of the 7th international conference on information and knowledge management, pp 148–155 Google Scholar
  7. Fireman Z, Glukhovsky A, Jacob H, Lavy A, Lewkowicz S, Scapa E (2002) Wireless capsule endoscopy. Israel Med Assoc J 4:717–719 Google Scholar
  8. Majewski P, Jedruch W (2005) Endoscopy images classification with kernel based learning algorithms. In: Innovations in applied artificial intelligence, pp 400–405 Google Scholar
  9. Osuna E, Freund R, Girosit F (1997) Training support vector machines: an application to face detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 130–136 Google Scholar
  10. Vapnik V (1995) The nature of statistical learning theory. Springer, New York MATHGoogle Scholar
  11. Vapnik V (1998) Statistical learning theory. Wiley, New York MATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Dept. of MathematicsUniv. of North TexasDentonUSA
  2. 2.Dept. of Computer Science and EngineeringUniv. of North TexasDentonUSA

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