Abstract
In this paper we propose an architecture for processing endoscopic procedures results. The goal is to create a complete system capable of processing any type of endoscopic multimedia results, in order to overcome the most common issues in the endoscopic domain (e.g. video’s long-duration, gastroenterologist’s possible difficulty to maintain the focus and efficiency during the viewing process, imperfections in images/videos). It was this scenario that led to the conception of the MIVprocessing solution, which will address these and other problems, providing an added value to the elaboration of diagnoses. The MIVprocessing is composed of five tasks: Video Summarization (elimination of the “non-informative” frames); Pre-Processing (correction/improvement of the frames); Pre-Detection; Segmentation; and Feature Extraction and Classification. The idea is to create a framework that brings together the capabilities of different but at the same time complementary concepts (e.g. image and signal processing, machine learning, computer vision). This conjugation applied to the endoscopic domain provides a set of features capable of improving the gastroenterologist’s activities during and after the procedure.
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
Moeslund, T.B.: Introduction to Video and Image Processing: Building Real Systems and Applications. Springer, London (2012)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson Education International (2009)
Bui, A.A.T., Taira, R.K., Kangarloo, H.: Introduction - What is Medical Imaging Informatics? In: Bui, A.A.T., Taira, R.K. (eds.) Medical Imaging Informatics, pp. 3–14. Springer US (2010)
Schiller, K.F.R., Warren, B.F., Hunt, R.H.: Atlas of Gastrointestinal Endoscopy and Related Pathology. Wiley-Blackwell (2002)
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)
Karargyris, A., Bourbakis, N.: Detection of Small Bowel Polyps and Ulcers in Wireless Capsule Endoscopy Videos. IEEE Trans. Biomed. Eng. 58, 2777–2786 (2011)
Alexandre, L.A., Casteleiro, J.M., Nobreinst, N.: Polyp Detection in Endoscopic Video Using SVMs. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 358–365. Springer, Heidelberg (2007)
Barbosa, D.C., Roupar, D.B., Ramos, J.C., Tavares, A.C., Lima, C.S.: Automatic Small Bowel Tumor Diagnosis by using Multi-scale Wavelet-based Analysis in Wireless Capsule Endoscopy Images. Biomed. Eng. Online. 11, 1–17 (2012)
Iakovidis, D.K., Maroulis, D.E., Karkanis, S.: a: An Intelligent System for Automatic Detection of Gastrointestinal Adenomas in Video Endoscopy. Comput. Biol. Med. 36, 1084–1103 (2006)
Chen, Y., Lee, J.: Ulcer Detection in Wireless Capsule Endoscopy Video. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 1181–1184. ACM (2012)
Pan, G., Yan, G., Qiu, X., Cui, J.: Bleeding Detection in Wireless Capsule Endoscopy Based on Probabilistic Neural Network. J. Med. Syst. 35, 1477–1484 (2011)
Li, B., Meng, M.Q.-H.: Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images. IEEE Trans. Biomed. Eng. 56, 1032–1039 (2009)
Stehle, T.: Removal of Specular Reflections in Endoscopic Images Removal of Specular Reflections in Endoscopic Images. Acta Polytech. J. Adv. Eng. 46, 32–36 (2006)
Bashar, M.K., Kitasaka, T., Suenaga, Y., Mekada, Y., Mori, K.: Automatic Detection of Informative Frames from Wireless Capsule Endoscopy Images. Med. Image Anal. 14, 449–470 (2010)
Lau, P.Y., Correia, P.L.: Analyzing Gastrointestinal Tissue Images using Multiple Features. In: Proceedings of the International Conference on Telecommunications, pp. 435–438 (2007)
Laranjo, I., Braga, J., Assunção, D., Silva, A., Rolanda, C., Lopes, L., Correia-Pinto, J., Alves, V.: Web-Based Solution for Acquisition, Processing, Archiving and Diffusion of Endoscopy Studies. In: Omatu, S., Neves, J., Rodriguez, J.M.C., Paz Santana, J.F., Gonzalez, S.R. (eds.) Distrib. Computing & Artificial Intelligence. AISC, vol. 217, pp. 317–324. Springer, Heidelberg (2013)
Braga, J., Laranjo, I., Assunção, D., Rolanda, C., Lopes, L., Correia-Pinto, J., Alves, V.: Endoscopic Imaging Results: Web based Solution with Video Diffusion. Procedia Technol. 9, 1123–1131 (2013)
Oliveira, T., Novais, P., Neves, J.: Guideline Formalization and Knowledge Representation for Clinical Decision Support. Adv. Distrib. Comput. Artif. Intell. J. 1, 1–12 (2012)
Aabakken, L., Rembacken, B., LeMoine, O., Kuznetsov, K., Rey, J.-F., Rösch, T., Eisen, G., Cotton, P., Fujino, M.: Minimal Standard Terminology for Gastrointestinal Endoscopy (MST 3.0) (2009)
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
Laranjo, I. et al. (2014). Video Processing Architecture: A Solution for Endoscopic Procedures Results. In: Ramos, C., Novais, P., Nihan, C., Corchado Rodríguez, J. (eds) Ambient Intelligence - Software and Applications. Advances in Intelligent Systems and Computing, vol 291. Springer, Cham. https://doi.org/10.1007/978-3-319-07596-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-07596-9_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07595-2
Online ISBN: 978-3-319-07596-9
eBook Packages: EngineeringEngineering (R0)