Abstract
One of the main goals of Wireless Capsule Endoscopy (WCE) is to detect the mucosal abnormalities such as blood, ulcer, polyp, and so on in the gastrointestinal tract. Only less than 5% of total 55,000 frames of a WCE video typically have abnormalities, so it is critical to develop a technique to automatically discriminate abnormal findings from normal ones. We introduce “Bag-of-Visual-Words” method which has been successfully used in particular for image classification in non-medical domains. Initially the training image patches are represented by color and texture features, and then the bag of words model is constructed by K-means clustering algorithm. Subsequently the document is represented as the histogram of the visual words which is the feature vector of the image. Finally, a SVM classifier is trained using these feature vectors to distinguish images with abnormal regions from ones without them. Experimental results on our current data set show that the proposed method achieves promising performances.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bresci, G., Parisi, G., Bertoni, M., Emanuele, T., Capria, A.: Video Capsule Endoscopy for Evaluating Obscure Gastrointestinal Bleeding and Suspected Small-Bowel Pathology. J. Gastroenterol 39, 803–806 (2004)
Li, B., Meng, M.Q.-H.: Wireless capsule endoscopy images enhancement using contrast driven forward and backward anisotropic diffusion. In: IEEE International Conference on Image Processing (ICIP), San Antonio, Texas, USA, vol. 2, pp. 437–440 (September 2007)
Li, B., Meng, M.Q.-H.: Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments. Computers in Biology and Medicine 39(2), 141–147 (2009)
Hwang, S., Celebi, M.E.: Polyp Detection in Wireless Capsule Endoscopy Videos Based on Image Segmentation and Geometric Feature. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP (2010)
Lee, J., Oh, J., Yuan, X., Tang, S.J.: Automatic Classification of Digestive Organs in Wireless Capsule Endoscopy Videos. In: Proc. of the ACM Symposium on Applied Computing, ACM SAC 2007, March 11 - 15 (2007)
Vigo, D.A.R., Khan, F.S., van de Weijer, J., Gevers, T.: The Impact of Color on Bag-of-Words Based Object Recognition. In: International Conference on Pattern Recognition (ICPR), pp. 1549–1553 (August 2010)
Gupta, S., Kim, J., Grauman, K., Mooney, R.J.: Watch, listen & learn: Co-training on captioned images and videos. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 457–472. Springer, Heidelberg (2008)
Li, W.-J., Yeung Localized, D.-Y.: content-based image retrieval through evidence region identification. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Miami Beach, Florida, USA, June 20-25 (2009)
Fei-Fei, L., Perona, P.: A Bayesian Hierarchical Model for Learning Natural Scene Categories. IEEE Comp. Vis. Patt. Recog. (2005)
Li, C.H.: Regularized color clustering in medical image database. IEEE Trans. Med. Imaging 19, 1150–1155 (2000)
Webster, M.A., De Valois, R.L.: Relationship between Spatial-Frequency and Orientation Tuning of Striate-Cortex Cells. J. Opt. Soc. Am. A 2, 1124–1132 (1985)
Vapnik, V.: Statistical learning theory. Wiley, Chichester (1998)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery 2(2), 121–167 (1998)
Keerthi, S.S., Lin, C.-J.: Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation 15(7), 1667–1689 (2003)
Dance, C., Willamowski, J., Fan, L.X., Bray, C., Csurka, G.: Visual categorization with bags of keypoints. In: ECCV Workshop on Statistical Learning in Computer Vision, pp. 1–22 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hwang, S. (2011). Bag-of-Visual-Words Approach to Abnormal Image Detection in Wireless Capsule Endoscopy Videos. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24031-7_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-24031-7_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24030-0
Online ISBN: 978-3-642-24031-7
eBook Packages: Computer ScienceComputer Science (R0)