Detection of Small Bowel Polyps and Ulcers in Wireless Capsule Endoscopy Videos

Over the last decade, wireless capsule endoscopy (WCE) technology has become a very useful tool for diagnosing diseases within the human digestive tract. Physicians using WCE can examine the digestive tract in a minimally invasive way searching for pathological abnormalities such as bleeding, polyps, ulcers, and Crohn's disease. To improve effectiveness of WCE, researchers have developed software methods to automatically detect these diseases at a high rate of success. This paper proposes a novel synergistic methodology for automatically discovering polyps (protrusions) and perforated ulcers in WCE video frames. Finally, results of the methodology are given and statistical comparisons are also presented relevant to other works.

[1]  Nikolaos G. Bourbakis,et al.  A methodology for detecting blood-based abnormalities in Wireless Capsule Endoscopy videos , 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.

[2]  Frans Vos,et al.  Lines of Curvature for Polyp Detection in Virtual Colonoscopy , 2006, IEEE Transactions on Visualization and Computer Graphics.

[3]  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.

[4]  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.

[5]  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.

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

[7]  T. Kanade,et al.  Color information for region segmentation , 1980 .

[8]  Miguel Tavares Coimbra,et al.  A Review of Current Computer Aided Diagnosis Systems for Polyp Detection in Virtual Colonoscopy , 2009 .

[9]  J. Daugman Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[10]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[11]  M. Yu M2A™ Capsule Endoscopy: A Breakthrough Diagnostic Tool for Small Intestine Imaging , 2002, Gastroenterology nursing : the official journal of the Society of Gastroenterology Nurses and Associates.

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  Ender Konukoglu,et al.  Polyp Enhancing Level Set Evolution of Colon Wall: Method and Pilot Study , 2007, IEEE Transactions on Medical Imaging.

[14]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[15]  Luís A. Alexandre,et al.  Color and Position versus Texture Features for Endoscopic Polyp Detection , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

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

[17]  Reena Sidhu,et al.  Capsule Endoscopy: Is There a Role for Nurses as Physician Extenders? , 2007, Gastroenterology nursing : the official journal of the Society of Gastroenterology Nurses and Associates.

[18]  R. L. de Valois,et al.  Relationship between spatial-frequency and orientation tuning of striate-cortex cells. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[19]  Ronald M. Summers,et al.  Colonic polyp segmentation in CT colonography-based on fuzzy clustering and deformable models , 2004, IEEE Transactions on Medical Imaging.

[20]  S. M. Steve SUSAN - a new approach to low level image processing , 1997 .

[21]  N. Bourbakis,et al.  A fuzzy technique for image segmentation of color images , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[22]  Baopu Li,et al.  Ulcer recognition in capsule endoscopy images by texture features , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[23]  Vassilis Kodogiannis,et al.  An adaptive neurofuzzy approach for the diagnosis in wireless capsule endoscopy imaging , 2007 .

[24]  J. Tasic,et al.  Colour spaces: perceptual, historical and applicational background , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[25]  Dimitris A. Karras,et al.  Computer-aided tumor detection in endoscopic video using color wavelet features , 2003, IEEE Transactions on Information Technology in Biomedicine.

[26]  Aly A. Farag,et al.  Geometric Features Based Framework for Colonic Polyp Detection using a New Color Coding Scheme , 2007, 2007 IEEE International Conference on Image Processing.

[27]  J. Raskin,et al.  Small bowel ulcers , 2001, Current treatment options in gastroenterology.

[28]  Ronald M. Summers,et al.  Polyp segmentation method for CT colonography computer-aided detection , 2003, SPIE Medical Imaging.

[29]  Carlos S. Lima,et al.  Automatic detection of small bowel tumors in capsule endoscopy based on color curvelet covariance statistical texture descriptors , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[31]  S. Abe,et al.  Fuzzy support vector machines for pattern classification , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).