Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments

The wireless capsule endoscopy (WCE) invented by Given Imaging has been gradually used in hospitals due to its great breakthrough that it can view the entire small bowel for gastrointestinal diseases. However, a tough problem associated with this new technology is that too many images to be examined by eyes cause a huge burden to physicians, so it is significant if we can help physicians do diagnosis using computerized methods. In this paper, a new method aimed for bleeding and ulcer detection in WCE images is proposed. This new approach mainly focuses on color feature, also a very powerful clue used by physicians for diagnosis, to judge the status of gastrointestinal tract. We propose a new idea of chromaticity moment as the features to discriminate normal regions and abnormal regions, which make full use of the Tchebichef polynomials and the illumination invariant of HSI color space, and we verify performances of the proposed features by employing neural network classifier. Experimental results on our present image data of bleeding and ulcer show that it is feasible to exploit the proposed chromaticity moments to detect bleeding and ulcer for WCE images.

[1]  R. Mukundan,et al.  Moment Functions in Image Analysis: Theory and Applications , 1998 .

[2]  Cheng Wang,et al.  Image analysis by modified Legendre moments , 2007, Pattern Recognit..

[3]  P. Schmid.  Segmentation of digitized dermatoscopic images by two-dimensional color clustering , 1999, IEEE Transactions on Medical Imaging.

[4]  Raveendran Paramesran,et al.  Image analysis by Krawtchouk moments , 2003, IEEE Trans. Image Process..

[5]  Pong C. Yuen,et al.  Regularized color clustering in medical image database , 2000, IEEE Transactions on Medical Imaging.

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

[7]  R. Mukundan,et al.  Some computational aspects of discrete orthonormal moments , 2004, IEEE Transactions on Image Processing.

[8]  Joost van de Weijer,et al.  Robust photometric invariant features from the color tensor , 2006, IEEE Transactions on Image Processing.

[9]  P. Wingo,et al.  Cancer statistics, 1997 , 1997, CA: a cancer journal for clinicians.

[10]  Roland T. Chin,et al.  On digital approximation of moment invariants , 1986, Computer Vision Graphics and Image Processing.

[11]  Begnaud Francis Hildebrand,et al.  Introduction to numerical analysis: 2nd edition , 1987 .

[12]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[13]  Simon K. Lo,et al.  The Clinical Utility of Wireless Capsule Endoscopy , 2004, Digestive Diseases and Sciences.

[14]  B. McNeil,et al.  Predicting Mortality after Coronary Artery Bypass Surgery , 1998, Medical decision making : an international journal of the Society for Medical Decision Making.

[15]  Sim Heng Ong,et al.  Image Analysis by Tchebichef Moments , 2001, IEEE Trans. Image Process..

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

[17]  T. Gevers,et al.  UvA-DARE ( Digital Academic Repository ) Robust Histogram Construction from Color Invariants for Object Recognition , 2003 .

[18]  Roziati Zainuddin,et al.  Pixel-based skin color detection technique , 2005 .

[19]  R. H. Moss,et al.  A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images. , 2003, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[20]  M. P. Tjoa,et al.  Feature extraction for the analysis of colon status from the endoscopic images , 2003, Biomedical engineering online.

[21]  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.

[22]  F. B. Hildebrand,et al.  Introduction To Numerical Analysis , 1957 .

[23]  Chee-Way Chong,et al.  Translation invariants of Zernike moments , 2003, Pattern Recognit..

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

[25]  Fernando Vilariño,et al.  Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[26]  G. Iddan,et al.  Wireless capsule endoscopy , 2003, Gut.

[27]  M. Nischik,et al.  Analysis of skin erythema using true-color images , 1997, IEEE Transactions on Medical Imaging.

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

[29]  Theo Gevers,et al.  Color feature detection and classification by learning , 2005, IEEE International Conference on Image Processing 2005.

[30]  George D. Magoulas,et al.  Tumor recognition in endoscopic video images using artificial neural network architectures , 2000, Proceedings of the 26th Euromicro Conference. EUROMICRO 2000. Informatics: Inventing the Future.

[31]  Norimichi Tsumura,et al.  Image-based skin color and texture analysis/synthesis by extracting hemoglobin and melanin information in the skin , 2003, ACM Trans. Graph..

[32]  Arnold W. M. Smeulders,et al.  Color Invariance , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Piotr J Durka,et al.  From wavelets to adaptive approximations: time-frequency parametrization of EEG , 2003, Biomedical engineering online.

[34]  Dimitris A. Karras,et al.  CoLD : a versatile detection system for colorectal lesions in endoscopy video-frames , 2002 .