Abstract
This paper proposes a novel hierarchical approach to improve the accuracy of the classification of normal-vs-abnormal frames in white-light colonoscopy videos. The existing approaches label each frame independently, without considering the temporal consistency between adjacent frames. Temporal consistency, however, can improve the classification accuracy in the presence of unclear/uncertain images. We propose to leverage temporal consistency between adjacent frames for colonoscopy video frame classification using a novel hierarchical classifier. Comparative experiments with five challenging full colonoscopy videos show that the proposed approach considerably improves the mean class normal/abnormal classification accuracy compared to the approaches where the frames are classified independently.
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
Cancer research UK. http://info.cancerresearchuk.org/cancerstats
Bressler, B., Paszat, L.F., Chen, Z., Rothwell, D.M., Vinden, C., Rabeneck, L.: Rates of new or missed colorectal cancers after colonoscopy and their risk factors: a population-based analysis. Gastroenterology 132(1), 96–102 (2007)
Lima, C., Barbosa, D., Ramos, A., Tavares, A., Montero, L., Carvalho, L.: Classification of endoscopic capsule images by using color wavelet features, higher order statistics and radial basis functions. In: IEEE EMBS (2008)
Manivannan, S., Wang, R., Trucco, E.: Extended gaussian-filtered local binary patterns for colonoscopy image classification. In: IEEE ICCV Workshops (2013)
Manivannan, S., Wang, R., Trucco, E., Hood, A.: Automatic normal-abnormal video frame classification for colonoscopy. In: IEEE ISBI (2013)
Engelhardt, S., Ameling, S., Paulus, D., Wirth, S.: Features for classification of polyps in colonoscopy. In: CEUR Workshop Proceedings (2010)
Karkanis, S.A., Iakovvidis, D.K., Maroulis, D.E., Karras, D.A., Tzivras, M.: Computer aided tumor detection in endoscopic video using color wavelet features. IEEE Trans. IT Biomed. 7, 141–152 (2003)
Maroulis, D.E., Iakovidis, D.K., Karkanis, S.A., Karras, D.A.: Cold: a versatile detection system for colorectal lesions in endoscopy video-frames. Comput. Methods Programs Biomed. 70, 151–166 (2003)
Cui, L., Hu, C., Zou, Y., Meng, M.Q.H.: Bleeding detection in wireless capsule endoscopy images by support vector classifier, IEEE International Conference on Information and Automation (2010)
Tjoa, M.P., Krishnan, S.: Feature extraction for the analysis of colon status from the endoscopic images. Biomed. Eng. Online 2, 3–17 (2003)
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 59, 355–362 (2012)
Liedlgruber, M., Uhl, A.: Computer-aided decision support systems for endoscopy in the gastrointestinal tract: a review. IEEE Rev. Biomed. Eng. 4, 73–88 (2011)
Ben-Hur, A., Weston, J.: A user’s guide to support vector machines. In: Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol. 609, pp. 223–239. Humana Press (2010)
Lin, H.T., Lin, C.J., Weng, R.: A note on platt’s probabilistic outputs for support vector machines. Mach. Learn. 68(3), 267–276 (2007)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: IEEE CVPR (2010)
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: I3A 1st Workshop on Pattern Recognition Techniques for Indirect Immunoflurescence Images, ICPR (2014)
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)
Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms (2008). http://www.vlfeat.org/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Puerto-Souza, G.A., Manivannan, S., Trujillo, M.P., Hoyos, J.A., Trucco, E., Mariottini, GL. (2016). Enhancing Normal-Abnormal Classification Accuracy in Colonoscopy Videos via Temporal Consistency. In: Luo, X., Reichl, T., Reiter, A., Mariottini, GL. (eds) Computer-Assisted and Robotic Endoscopy. CARE 2015. Lecture Notes in Computer Science(), vol 9515. Springer, Cham. https://doi.org/10.1007/978-3-319-29965-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-29965-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29964-8
Online ISBN: 978-3-319-29965-5
eBook Packages: Computer ScienceComputer Science (R0)