Improved Bag of Feature for Automatic Polyp Detection in Wireless Capsule Endoscopy Images

Wireless capsule endoscopy (WCE) needs computerized method to reduce the review time for its large image data. In this paper, we propose an improved bag of feature (BoF) method to assist classification of polyps in WCE images. Instead of utilizing a single scale-invariant feature transform (SIFT) feature in the traditional BoF method, we extract different textural features from the neighborhoods of the key points and integrate them together as synthetic descriptors to carry out classification tasks. Specifically, we study influence of the number of visual words, the patch size and different classification methods in terms of classification performance. Comprehensive experimental results reveal that the best classification performance is obtained with the integrated feature strategy using the SIFT and the complete local binary pattern (CLBP) feature, the visual words with a length of 120, the patch size of 8*8, and the support vector machine (SVM). The achieved classification accuracy reaches 93.2%, confirming that the proposed scheme is promising for classification of polyps in WCE images.

[1]  Nikolaos G. Bourbakis,et al.  Detection of Small Bowel Polyps and Ulcers in Wireless Capsule Endoscopy Videos , 2011, IEEE Transactions on Biomedical Engineering.

[2]  Nikolaos G. Bourbakis,et al.  Three-Dimensional Reconstruction of the Digestive Wall in Capsule Endoscopy Videos Using Elastic Video Interpolation , 2011, IEEE Transactions on Medical Imaging.

[3]  Chong-Wah Ngo,et al.  Towards optimal bag-of-features for object categorization and semantic video retrieval , 2007, CIVR '07.

[4]  Mohammad Reza Zare,et al.  Automatic classification of medical X-ray images using a bag of visual words , 2013, IET Comput. Vis..

[5]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[6]  Max Q.-H. Meng,et al.  A novel feature for polyp detection in wireless capsule endoscopy images , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Mitchell S Cappell,et al.  The pathophysiology, clinical presentation, and diagnosis of colon cancer and adenomatous polyps. , 2005, The Medical clinics of North America.

[8]  Jacques Wainer,et al.  Assessing the Need for Referral in Automatic Diabetic Retinopathy Detection , 2013, IEEE Transactions on Biomedical Engineering.

[9]  Tao Mei,et al.  Contextual Bag-of-Words for Visual Categorization , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Aymeric Histace,et al.  Towards real-time in situ polyp detection in WCE images using a boosting-based approach , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[11]  Dimitris K. Iakovidis,et al.  Automatic lesion detection in capsule endoscopy based on color saliency: closer to an essential adjunct for reviewing software. , 2014, Gastrointestinal endoscopy.

[12]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[13]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[14]  Sun Young Park,et al.  A Colon Video Analysis Framework for Polyp Detection , 2012, IEEE Transactions on Biomedical Engineering.

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  Chih-Fong Tsai,et al.  Bag-of-Words Representation in Image Annotation: A Review , 2012 .

[17]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[18]  Max Q.-H. Meng,et al.  Polyp classification based on Bag of Features and saliency in wireless capsule endoscopy , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[19]  H. Soltanian-Zadeh,et al.  Polyp detection in Wireless Capsule Endoscopy images by using region-based active contour model , 2012, 2012 19th Iranian Conference of Biomedical Engineering (ICBME).

[20]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[21]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[22]  Nicholas Ayache,et al.  Learning Semantic and Visual Similarity for Endomicroscopy Video Retrieval , 2012, IEEE Transactions on Medical Imaging.

[23]  Max Q.-H. Meng,et al.  Automatic polyp detection for wireless capsule endoscopy images , 2012, Expert Syst. Appl..

[24]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[26]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Aymeric Histace,et al.  Towards a multimodal wireless video capsule for detection of colonic polyps as prevention of colorectal cancer , 2013, 13th IEEE International Conference on BioInformatics and BioEngineering.