Current Challenges on Polyp Detection in Colonoscopy Videos: From Region Segmentation to Region Classification. a Pattern Recognition-based Approach

In this paper we present our approach on selection of regions of interest in colonoscopy videos, which consists of three stages: Region Segmentation, Region Description and Region Classification, focusing on the Region Segmentation stage. As part of our segmentation scheme, we introduce our region merging algorithm that takes into account our model of appearance of the polyp. As the results show, the output of this stage reduces the number of final regions and indicates the degree of information of these regions. Our approach appears to outperform state-of-the-art methods. Our results can be used to identify polypcontaining regions in the later stages.

[1]  Fernando Pellon de Miranda,et al.  The semivariogram in comparison to the co-occurrence matrix for classification of image texture , 1998, IEEE Trans. Geosci. Remote. Sens..

[2]  Danica Kragic,et al.  Grasping familiar objects using shape context , 2009, 2009 International Conference on Advanced Robotics.

[3]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[4]  Malay Kumar Kundu,et al.  Interactive Image Retrieval in a Fuzzy Framework , 2009, WILF.

[5]  Robert R Cima,et al.  Effectiveness of a Multimedia-Based Educational Intervention for Improving Colon Cancer Literacy in Screening Colonoscopy Patients , 2010, Diseases of the colon and rectum.

[6]  Alireza Khotanzad,et al.  Invariant Image Recognition by Zernike Moments , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Matti Pietikäinen,et al.  Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2000, ECCV.

[9]  Faouzi Kossentini,et al.  Local motion descriptors , 2001, 2001 IEEE Fourth Workshop on Multimedia Signal Processing (Cat. No.01TH8564).

[10]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[11]  Matti Pietikäinen,et al.  An Experimental Comparison of Autoregressive and Fourier-Based Descriptors in 2D Shape Classification , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Brian V. Funt,et al.  Color Constant Color Indexing , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  A. Chalechale,et al.  Edge image description using angular radial partitioning , 2004 .

[15]  Ling-Hwei Chen,et al.  An efficient computation method for the texture browsing descriptor of MPEG-7 , 2005, Image Vis. Comput..

[16]  Joan Serrat,et al.  Evaluation of Methods for Ridge and Valley Detection , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[18]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[19]  Dengsheng Zhang,et al.  An Efficient and Robust Technique for Region Based Shape Representation and Retrieval , 2007, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007).

[20]  Xiaosheng Wu,et al.  Shape Retrieval Based on the Relativity of Chain Codes , 2007, MCAM.

[21]  Rita Cucchiara,et al.  Color Features Performance Comparison for Image Retrieval , 2009, ICIAP.

[22]  C.-C. Jay Kuo,et al.  Wavelet descriptor of planar curves: theory and applications , 1996, IEEE Trans. Image Process..

[23]  Sergio Escalera,et al.  Handwritten Symbol Recognition by a Boosted Blurred Shape Model with Error Correction , 2007, IbPRIA.