Abstract
We propose a novel classification framework called the video-specific SVM (V-SVM) for normal-vs-abnormal white-light colonoscopy image classification. V-SVM is an ensemble of linear SVMs, with each trained to separate the abnormal images in a particular video from all the normal images in all the videos. Since V-SVM is designed to capture lesion-specific properties as well as intra-class variations it is expected to perform better than SVM. Experiments on a colonoscopy image dataset with about \(10{,}000\) images show that V-SVM significantly improves the performance over SVM and other baseline classifiers.
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
References
Winawer, S.J.: Colorectal cancer screening. Best Pract. Res. Clin Gastroenterol. 21(6), 1031–1048 (2007)
Wallace, M.B.: Improving colorectal adenoma detection: technology or technique? Gastroenterology 132, 1221–1223 (2007)
Manivannan, S., Wang, R., Trucco, E., Hood, A.: Automatic normal-abnormal video frame classification for colonoscopy. In: IEEE International Symposium on Biomedical Imaging (2013)
Manivannan., S., Wang, R., Trucco, E.: Extended gaussian-filtered local binary patterns for colonoscopy image classification. In: IEEE International Conference on Computer Vision Workshops (2013)
Kumar, R., Zhao, Q., Seshamani, S., Mullin, G., Hanger, G., Dassopoulos, T.: Assessment of crohn’s disease lesions in wireless capsule endoscopy images. Biomed. Eng. Online 11, 59 (2012)
Bejakovic, S., Kumar, R., Dassopoulos, T., Gerard Mullin, G.H.: Analysis of crohn’s disease lesions in capsule endoscopy images. In: IEEE International Conference on Robotics and Automation (2009)
Li, P., Chan, K.L., Krishnan, S.: Learning a multi-size patch-based hybrid kernel machine ensemble for abnormal region detection in colonoscopic images. In: IEEE Conference on Computer Vision and Pattern Recognition (2005)
Li, P., Chan, K.L., Krishnan, S., Gao, Y.: Detecting abnormal regions in colonoscopic images by patch-based classifier ensemble. In: International Conference on Pattern Recognition (2004)
Zhao, Q., Meng, M.H.: Polyp detection in wireless capsule endoscopy images using novel color texture features. In: World Congress on Intelligent Control and Automation (2011)
Shan, Y., Han, F., Sawhney, H., Kumar, R.: Learning exemplar-based categorization for the detection of multi-view multi-pose objects. In: IEEE Computer Vision and Pattern Recognition (2006)
Li, S.Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H., Shum, H.: Statistical learning of multi-view face detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 67–81. Springer, Heidelberg (2002)
Viola, M., Jones, M.J., Viola, P.: Fast multi-view face detection. In: Computer Vision and Pattern Recognition (2003)
Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Trans. Pattern Anal. Mach. Intell. 23, 349–361 (2001)
Malisiewicz, T., Gupta, A., Efros, A.A.: Ensemble of exemplar-svms for object detection and beyond. In: IEEE International Conference on Computer Vision (2011)
Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola, A.J., Bartlett, P., Scholkopf, B., Schuurmans, D. (eds.) Advances in Large Margin Classifiers. MIT Press, Cambridge (1999)
Arandjelović, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: IEEE Computer Vision and Pattern Recognition (2012)
Manivannan, S., Li, W., Akbar, S., Wang, R., Zhang, J., McKenna, S.J.: Hep-2 cell classification using multi-resolution local patterns and ensemble SVMs. In: ICPR I3A Workshop on Pattern Recognition Techniques for IIF Images (2014)
Manivannan, S., Li, W., Akbar, S., Wang, R., Zhang, J., McKenna, S.J.: Hep-2 specimen classification using multi-resolution local patterns and SVM. In: ICPR I3A Workshop on Pattern Recognition Techniques for IIF Images (2014)
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: IEEE Computer Vision and Pattern Recognition (2010)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Chatfield, K., Lempitsky, V., Vedaldi, A., Zisserman, A.: The devil is in the details: an evaluation of recent feature encoding methods. In: British Machine Vision Conference (2011)
Kim, H.-C., Pang, S., Je, H.-M., Kim, D., Bang, S.-Y.: Support vector machine ensemble with bagging. In: Lee, S.-W., Verri, A. (eds.) SVM 2002. LNCS, vol. 2388, pp. 397–408. Springer, Heidelberg (2002)
Acknowledgement
This work is funded by 2011–2016 EU FP7 ERC project “CODIR: colonic disease investigation by robotic hydrocolonoscopy”, collaborative between the Universities of Dundee (PI Prof Sir A Cuschieri) and Leeds (PI Prof A Neville).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Manivannan, S., Wang, R., Trujillo, M.P., Hoyos, J.A., Trucco, E. (2014). Video-Specific SVMs for Colonoscopy Image Classification. In: Luo, X., Reichl, T., Mirota, D., Soper, T. (eds) Computer-Assisted and Robotic Endoscopy. CARE 2014. Lecture Notes in Computer Science(), vol 8899. Springer, Cham. https://doi.org/10.1007/978-3-319-13410-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-13410-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13409-3
Online ISBN: 978-3-319-13410-9
eBook Packages: Computer ScienceComputer Science (R0)