Abstract
Wireless Capsule Endoscopy(WCE) is a revolutionary technique for visualizing patient’s entire digestive tract. But, the analysis of a huge number of images produced during an examination of a patient is hindering the application of WCE. In this direction, we automated the process of bleeding detection in WCE images based on improved Bag of Visual Words (BoVW). Two feature integration schemes have been explored. Experimental results show that the best classification performance is obtained using integration of SIFT and uniform LBP features. The highest classification accuracy achieved is 95.06 % for a visual vocabulary of length 100. Results reveal that the proposed methodology is discriminating enough to classify bleeding images.
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Acknowledgement
This work was supported in part by CMUC – UID/MAT/00324/2013, funded by FCT/MCTES (Portugal) and co-funded by the European Regional Development Fund through the Partnership Agreement PT2020.
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Joshi, I., Kumar, S., Figueiredo, I.N. (2016). Bag of Visual Words Approach for Bleeding Detection in Wireless Capsule Endoscopy Images. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_64
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DOI: https://doi.org/10.1007/978-3-319-41501-7_64
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