Wireless Capsule Endoscopy Color Video Segmentation

This paper describes the use of color image analysis to automatically discriminate between oesophagus, stomach, small intestine, and colon tissue in wireless capsule endoscopy (WCE). WCE uses ldquopill-camrdquo technology to recover color video imagery from the entire gastrointestinal tract. Accurately reviewing and reporting this data is a vital part of the examination, but it is tedious and time consuming. Automatic image analysis tools play an important role in supporting the clinician and speeding up this process. Our approach first divides the WCE image into subimages and rejects all subimages in which tissue is not clearly visible. We then create a feature vector combining color, texture, and motion information of the entire image and valid subimages. Color features are derived from hue saturation histograms, compressed using a hybrid transform, incorporating the discrete cosine transform and principal component analysis. A second feature combining color and texture information is derived using local binary patterns. The video is segmented into meaningful parts using support vector or multivariate Gaussian classifiers built within the framework of a hidden Markov model. We present experimental results that demonstrate the effectiveness of this method.

[1]  Ram D. Sriram,et al.  Model of Deformable Rings for Aiding the Wireless Capsule Endoscopy Video Interpretation and Reporting , 2004, ICCVG.

[2]  Alexander J. Smola,et al.  Advances in Large Margin Classifiers , 2000 .

[3]  J. Rey,et al.  The Role of Video Capsule Endoscopy in the Diagnosis of Digestive Diseases: a Review of Current Possibilities , 2004, Endoscopy.

[4]  J. Herrerias,et al.  The value of capsule endoscopy in pediatric patients with a suspicion of Crohn's disease. , 2004, Endoscopy.

[5]  Duncan Bell,et al.  Stomach, intestine, and colon tissue discriminators for wireless capsule endoscopy images , 2005, SPIE Medical Imaging.

[6]  Miguel Tavares Coimbra,et al.  Combining Color with Spatial and Temporal Position of the Endoscopic Capsule for Improved Topographic Classification and Segmentation , 2006, SAMT.

[7]  Fernando Vilariño,et al.  Identification of Intestinal Motility Events of Capsule Endoscopy Video Analysis , 2005, ACIVS.

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

[9]  Stefan M. Rüger,et al.  Medical Image Retrieval Using Texture, Locality and Colour , 2004, CLEF.

[10]  Miguel Tavares Coimbra,et al.  Automated Topographic Segmentation and Transit Time Estimation in Endoscopic Capsule Exams , 2008, IEEE Transactions on Medical Imaging.

[11]  Shih-Fu Chang,et al.  Structure analysis of soccer video with domain knowledge and hidden Markov models , 2004, Pattern Recognit. Lett..

[12]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[13]  Lai-Man Po,et al.  A novel four-step search algorithm for fast block motion estimation , 1996, IEEE Trans. Circuits Syst. Video Technol..

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

[15]  Stefan Rüping,et al.  A Simple Method For Estimating Conditional Probabilities For SVMs , 2004, LWA.

[16]  R. Schoefl,et al.  Multicenter Retrospective Evaluation of Capsule Endoscopy in Clinical Routine , 2004, Endoscopy.

[17]  P. Wang,et al.  Classification of endoscopic images based on texture and neural network , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  Gregory B Haber,et al.  Capsule endoscopy regional transit abnormality: a sign of underlying small bowel pathology. , 2003, Gastrointestinal endoscopy.

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

[20]  Jianhua Lu,et al.  A simple and efficient search algorithm for block-matching motion estimation , 1997, IEEE Trans. Circuits Syst. Video Technol..

[21]  Philippe Refregier,et al.  PROBABILISTIC APPROACH FOR MULTICLASS CLASSIFICATION WITH NEURAL NETWORKS , 1991 .

[22]  Mark H. Fisher,et al.  Bleeding detection in wireless capsule endoscopy using adaptive colour histogram model and support vector classification , 2008, SPIE Medical Imaging.

[23]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[24]  Ye Xu,et al.  MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies , 2006, IEEE Transactions on Medical Imaging.

[25]  C. H. Chen,et al.  Handbook of Pattern Recognition and Computer Vision , 1993 .

[26]  L. R. Rabiner,et al.  An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition , 1983, The Bell System Technical Journal.

[27]  Fernando Vilariño,et al.  ROC curves and video analysis optimization in intestinal capsule endoscopy , 2006, Pattern Recognit. Lett..

[28]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[29]  Hui-Tang Lin,et al.  A note on platt''s probabilistic outputs for support vector machine. Technical report , 2003 .

[30]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[31]  Miguel Tavares Coimbra,et al.  Topographic Segmentation and Transit Time Estimation for Endoscopic Capsule Exams , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[32]  Kai-Kuang Ma,et al.  Adaptive irregular pattern search with zero-motion prejudgement for fast block-matching motion estimation , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[33]  Moshe Rubin,et al.  The value of wireless capsule endoscopy in patients with complicated celiac disease. , 2005, Gastrointestinal endoscopy.

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

[35]  Kai-Kuang Ma,et al.  A new diamond search algorithm for fast block-matching motion estimation , 2000, IEEE Trans. Image Process..

[36]  W. Qureshi.  Current and future applications of the capsule camera , 2004, Nature Reviews Drug Discovery.

[37]  Miguel Tavares Coimbra,et al.  Extracting clinical information from endoscopic capsule exams using MPEG-7 visual descriptors , 2005 .

[38]  Matti Pietikäinen,et al.  Classification with color and texture: jointly or separately? , 2004, Pattern Recognit..

[39]  E. Rondonotti,et al.  Sensitivity and Specificity of the Suspected Blood Identification System in Video Capsule Enteroscopy , 2005, Endoscopy.

[40]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[41]  John S. Boreczky,et al.  A hidden Markov model framework for video segmentation using audio and image features , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[42]  Van Nostrand,et al.  Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm , 1967 .

[43]  David Connah,et al.  Using Local Binary Pattern Operators for Colour Constant Image Indexing , 2006, CGIV.

[44]  D. Fischer,et al.  Capsule endoscopy: the localization system. , 2004, Gastrointestinal endoscopy clinics of North America.

[45]  P. Swain,et al.  Wireless capsule endoscopy and Crohn’s disease , 2005, Gut.

[46]  Robert Tibshirani,et al.  Classification by Pairwise Coupling , 1997, NIPS.

[47]  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.