Colon Polyp Segmentation Using Texture Analysis

In recent years, there has been an increase in the number of detected cases of colon cancer. This is largely due to the increasing use of colorectal screening. The number of colonoscopies performed has consequently risen, leading to specialists often being overburdened by the increasing workload. In an attempt to provide assistance with medical diagnosis, algorithms have been developed which enable the automated detection of polyps in colonoscopy images via the analysis of their texture. This aims to reduce the missed polyp rate, which is the main problem regarding colonoscopy. The system designed herein has obtained an 89.21% polyp detection rate, and 93.9% and 87.31% segmentation quality for the annotated area covered and Dice coefficient indicators, respectively. The development of this method aims to provide a support tool for medical diagnosis which has a positive impact on patient health.

[1]  Nikolaos G. Bourbakis,et al.  Detection of Small Bowel Polyps and Ulcers in Wireless Capsule Endoscopy Videos , 2011, IEEE Transactions on Biomedical Engineering.

[2]  Fernando Vilariño,et al.  Depth of Valleys Accumulation Algorithm for Object Detection , 2011, CCIA.

[3]  Jung-Hwan Oh,et al.  Part-Based Multiderivative Edge Cross-Sectional Profiles for Polyp Detection in Colonoscopy , 2014, IEEE Journal of Biomedical and Health Informatics.

[4]  Nima Tajbakhsh,et al.  Automatic polyp detection from learned boundaries , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[5]  Kuk-Jin Yoon,et al.  Polyp Detection via Imbalanced Learning and Discriminative Feature Learning , 2015, IEEE Transactions on Medical Imaging.

[6]  Wei-Chih Shen,et al.  Tumor detecting in colonoscopic narrow-band imaging data , 2012, 2012 International Symposium on Intelligent Signal Processing and Communications Systems.

[7]  Day Dw.  The adenoma-carcinoma sequence. , 1978 .

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

[9]  Fernando Vilariño,et al.  Polyp Segmentation Method in Colonoscopy Videos by Means of MSA-DOVA Energy Maps Calculation , 2014, CLIP@MICCAI.

[10]  Fernando Vilariño,et al.  Impact of image preprocessing methods on polyp localization in colonoscopy frames , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[11]  Emanuele Trucco,et al.  Automatic normal-abnormal video frame classification for colonoscopy , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[12]  Fernando Vilariño,et al.  Integration of Valley Orientation Distribution for Polyp Region Identification in Colonoscopy , 2011, Abdominal Imaging.

[13]  Dimitrios K. Iakovidis,et al.  An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy , 2006, Comput. Biol. Medicine.

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

[15]  Mingda Zhou,et al.  Polyp detection and radius measurement in small intestine using video capsule endoscopy , 2014, 2014 7th International Conference on Biomedical Engineering and Informatics.

[16]  Fernando Vilariño,et al.  A Region Segmentation Method for Colonoscopy Images Using a Model of Polyp Appearance , 2011, IbPRIA.

[17]  José M. Bioucas-Dias,et al.  Segmentation and Detection of Colorectal Polyps Using Local Polynomial Approximation , 2012, ICIAR.

[18]  Jung-Hwan Oh,et al.  Polyp Detection in Colonoscopy Video using Elliptical Shape Feature , 2007, 2007 IEEE International Conference on Image Processing.

[19]  Nojun Kwak,et al.  Polyp detection in Colonoscopy Videos Using Deeply-Learned Hierarchical Features , 2015 .

[20]  Basanna V. Dhandra,et al.  Analysis of Abnormality in Endoscopic images using Combined HSI Color Space and Watershed Segmentation , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

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

[22]  Jesfis Peral,et al.  Heuristics -- intelligent search strategies for computer problem solving , 1984 .

[23]  Gerard Lacey,et al.  Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging , 2010, EURASIP J. Image Video Process..

[24]  Nima Tajbakhsh,et al.  A Classification-Enhanced Vote Accumulation Scheme for Detecting Colonic Polyps , 2013, Abdominal Imaging.

[25]  Luís A. Alexandre,et al.  Polyp Detection in Endoscopic Video Using SVMs , 2007, PKDD.

[26]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[27]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[28]  Max Q.-H. Meng,et al.  Comparison of Several Texture Features for Tumor Detection in CE Images , 2012, Journal of Medical Systems.

[29]  Fernando Vilariño,et al.  Texture-Based Polyp Detection in Colonoscopy , 2009, Bildverarbeitung für die Medizin.

[30]  Yuji Iwahori,et al.  Automatic Polyp Detection in Endoscope Images Using a Hessian Filter , 2013, MVA.

[31]  Nima Tajbakhsh,et al.  Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information , 2016, IEEE Transactions on Medical Imaging.

[32]  Sun Young Park,et al.  A Colon Video Analysis Framework for Polyp Detection , 2012, IEEE Transactions on Biomedical Engineering.

[33]  Fernando Vilariño,et al.  WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians , 2015, Comput. Medical Imaging Graph..

[34]  Fernando Vilariño,et al.  Towards automatic polyp detection with a polyp appearance model , 2012, Pattern Recognit..

[35]  Gregory G. Slabaugh,et al.  Automatic Segmentation of Polyps in Colonoscopic Narrow-Band Imaging Data , 2012, IEEE Transactions on Biomedical Engineering.