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Multi-scale completed local binary patterns for ulcer detection in wireless capsule endoscopy images

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Abstract

This paper deals with ulcer abnormalities detection of small bowel, from wireless capsule endoscopy images (WCE). We propose a multi-scale approach based on completed local binary patterns, and laplacian pyramid (MS-CLBP). The proposed approach captures additional information about the magnitude as a robust descriptor against illuminations changes in WCE images. In addition, ulcer detection, was performed using the Green component and Cr components of RGB and YCbCr color spaces, respectively. Using the support vector machine (SVM) classifier, we conduct several experiments on two datasets. The results obtained validate the efficiency of the proposed system with an average accuracy of 95.11 and 93.88% for both datasets. Finally, a comparison with the state of the art methods shows that the proposed method is superior to the other approaches.

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Acknowledgements

The authors would like to thank the Cheik Zaid Hospital for sharing the WCE images. In addition, the authors would like to acknowledge and thank Dr. Meryem BENNANI, the responsible of the gastroenterology department, and Dr. Hasnae AHENDAR for their assistance and technical comments, as well as their professional suggestions during the preparation of the dataset.

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Authors and Affiliations

  1. LabSIV, Department of Computer Science, Faculty of Science, Ibn Zohr University, BP 8106, 80000, Agadir, Morocco

    Meryem Souaidi, Abdelkaher Ait Abdelouahed & Mohamed El Ansari

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  1. Meryem Souaidi
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  2. Abdelkaher Ait Abdelouahed
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  3. Mohamed El Ansari
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Correspondence to Meryem Souaidi.

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Souaidi, M., Abdelouahed, A.A. & El Ansari, M. Multi-scale completed local binary patterns for ulcer detection in wireless capsule endoscopy images. Multimed Tools Appl 78, 13091–13108 (2019). https://doi.org/10.1007/s11042-018-6086-2

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  • DOI: https://doi.org/10.1007/s11042-018-6086-2

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