A strategy to abstract WCE video clips based on LDA

Wireless Capsule Endoscopy (WCE) is a novel technique that allows visualization of the whole gastrointestinal (GI) tract especially the small intestine in a comfortable, non-invasive and efficacious way. The main disadvantage of WCE is that physicians need to examine a video of over 55,000 frames which is a time-consuming and labor intensive task. To address the problem, a strategy of WCE video clip abstraction based on linear discriminant analysis (LDA) is proposed in this paper. We extract multiple features based on which the frame differences are measured. Then the video clips are segmented using a non-parametric key-point detection algorithm and finally the most representative frames (MRFs) are extracted based on LDA algorithm. Experimental results demonstrate the proposed strategy achieves promising performances.

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

[2]  Minh N. Do,et al.  Contourlets: a directional multiresolution image representation , 2002, Proceedings. International Conference on Image Processing.

[3]  De Xu,et al.  Illumination-independent descriptors using color moment invariants , 2009 .

[4]  Dimitrios K. Iakovidis,et al.  Automatic frame reduction of Wireless Capsule Endoscopy video , 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.

[5]  Guojun Lu,et al.  Shape-based image retrieval using generic Fourier descriptor , 2002, Signal Process. Image Commun..

[6]  D. S. Guru,et al.  Efficient Non-Parametric Corner Detection: An Approach Based on Small Eigenvalue , 2007, Fourth Canadian Conference on Computer and Robot Vision (CRV '07).

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

[8]  R. Dinesh,et al.  Non-parametric adaptive region of support useful for corner detection: a novel approach , 2004, Pattern Recognit..

[9]  Susan T. Dumais,et al.  Using Linear Algebra for Intelligent Information Retrieval , 1995, SIAM Rev..

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

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

[12]  Raimondo Schettini,et al.  Erratum to: An innovative algorithm for key frame extraction in video summarization , 2006, Journal of Real-Time Image Processing.

[13]  Max Q.-H. Meng,et al.  Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments , 2009, Comput. Biol. Medicine.

[14]  Michal Mackiewicz,et al.  Wireless Capsule Endoscopy Color Video Segmentation , 2008, IEEE Transactions on Medical Imaging.

[15]  Joemon M. Jose,et al.  Shot Boundary Detection Based on Eigen Coefficients and Small Eigen Value , 2009, SAMT.

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

[17]  Jonathan Foote,et al.  Discriminative techniques for keyframe selection , 2005, 2005 IEEE International Conference on Multimedia and Expo.

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