Computer-Aided Bleeding Detection in WCE Video

Wireless capsule endoscopy (WCE) can directly take digital images in the gastrointestinal tract of a patient. It has opened a new chapter in small intestine examination. However, a major problem associated with this technology is that too many images need to be manually examined by clinicians. Currently, there is no standard for capsule endoscopy image interpretation and classification. Most state-of-the-art CAD methods often suffer from poor performance, high computational cost, or multiple empirical thresholds. In this paper, a new method for rapid bleeding detection in the WCE video is proposed. We group pixels through superpixel segmentation to reduce the computational complexity while maintaining high diagnostic accuracy. Feature of each superpixel is extracted using the red ratio in RGB space and fed into support vector machine for classification. Also, the influence of edge pixels has been removed in this paper. Comparative experiments show that our algorithm is superior to the existing methods in terms of sensitivity, specificity, and accuracy.

[1]  Vassilis Kodogiannis,et al.  Intelligent systems for computer-assisted clinical endoscopic image analysis , 2004 .

[2]  Jianguo Liu,et al.  Obscure bleeding detection in endoscopy images using support vector machines , 2009 .

[3]  D. Rex,et al.  Performance of given suspected blood indicator , 2003, American Journal of Gastroenterology.

[4]  Sven J. Dickinson,et al.  TurboPixels: Fast Superpixels Using Geometric Flows , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  G. Eisen,et al.  10 years of capsule endoscopy: an update , 2010, Expert review of gastroenterology & hepatology.

[6]  Max Q.-H. Meng,et al.  Tumor Recognition in Wireless Capsule Endoscopy Images Using Textural Features and SVM-Based Feature Selection , 2012, IEEE Transactions on Information Technology in Biomedicine.

[7]  Jitendra Malik,et al.  Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[8]  Max Q.-H. Meng,et al.  Wireless capsule endoscopy images enhancement via adaptive contrast diffusion , 2012, J. Vis. Commun. Image Represent..

[9]  Max Q.-H. Meng,et al.  Computer-aided small bowel tumor detection for capsule endoscopy , 2011, Artif. Intell. Medicine.

[10]  Jung-Hwan Oh,et al.  Blood detection in wireless capsule endoscopy using expectation maximization clustering , 2006, SPIE Medical Imaging.

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

[12]  P. Swain,et al.  Wireless capsule endoscopy. , 2002, The Israel Medical Association journal : IMAJ.

[13]  Jeff Berens,et al.  Image indexing using compressed colour histograms , 2000 .

[14]  Zhang Zhuo,et al.  Feature selection and classification for Wireless Capsule Endoscopic frames , 2009, 2009 International Conference on Biomedical and Pharmaceutical Engineering.

[15]  Jong Hyo Kim,et al.  Active Blood Detection in a High Resolution Capsule Endoscopy using Color Spectrum Transformation , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[16]  Abdelshakour A. Abuzneid,et al.  Detection of Bleeding in Wireless Capsule Endoscopy Images Using Range Ratio Color , 2010, ArXiv.

[17]  Guozheng Yan,et al.  Bleeding Detection in Wireless Capsule Endoscopy Based on Probabilistic Neural Network , 2011, Journal of Medical Systems.

[18]  N. Bourbakis,et al.  Wireless Capsule Endoscopy and Endoscopic Imaging: A Survey on Various Methodologies Presented , 2010, IEEE Engineering in Medicine and Biology Magazine.

[19]  Phooi Yee Lau,et al.  Detection of bleeding patterns in WCE video using multiple features , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  B. Lewis,et al.  AGA technical review on the evaluation and management of occult and obscure gastrointestinal bleeding. , 2000, Gastroenterology.

[21]  A. Uhl,et al.  Computer-Aided Decision Support Systems for Endoscopy in the Gastrointestinal Tract: A Review , 2011, IEEE Reviews in Biomedical Engineering.

[22]  Bill Buckles,et al.  Bleeding detection from capsule endoscopy videos , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[24]  Max Q.-H. Meng,et al.  Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images , 2009, IEEE Transactions on Biomedical Engineering.

[25]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Aman Ali,et al.  Video capsule endoscopy: a voyage beyond the end of the scope. , 2004, Cleveland Clinic journal of medicine.

[28]  J. Rey,et al.  Future Perspectives for Esophageal and Colorectal Capsule Endoscopy: Drems or Reality? , 2008 .

[29]  Miguel Tavares Coimbra,et al.  MPEG-7 Visual Descriptors—Contributions for Automated Feature Extraction in Capsule Endoscopy , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[30]  Enrico Magli,et al.  A technique for blood detection in wireless capsule endoscopy images , 2009, 2009 17th European Signal Processing Conference.

[31]  Max Q.-H. Meng,et al.  Texture analysis for ulcer detection in capsule endoscopy images , 2009, Image Vis. Comput..

[32]  Gencheng Guo,et al.  Bleeding region detection in WCE images based on color features and neural network , 2011, 2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS).