Texture and color based image segmentation and pathology detection in capsule endoscopy videos

Computer Methods and Programs in Biomedicine

Volumn 113, Issue 1, 2014, Pages 396-411

  Szczypiński, Piotr ^a   Klepaczko, Artur ^a   Pazurek, Marek ^b   Daniel, Piotr ^b  

^a LODZ UNIVERSITY OF TECHNOLOGY   (Poland)

^b MEDICAL UNIVERSITY OF LODZ   (Poland)

Author keywords

Capsule endoscopy; Feature selection; Medical image analysis; Texture analysis

Indexed keywords

CAPSULE ENDOSCOPY; CLASSIFICATION ALGORITHM; COLOR-BASED IMAGE SEGMENTATIONS; FEATURE SELECTION METHODS; GASTROINTESTINAL TRACT; RECOGNITION STRATEGIES; TEXTURE ANALYSIS; WIRELESS CAPSULE ENDOSCOPY IMAGE (WCE);

ENDOSCOPY; FEATURE EXTRACTION; MEDICAL IMAGING; PATHOLOGY; TEXTURES;

IMAGE TEXTURE;

ANALYSIS OF VARIANCE; ARTICLE; CAPSULE ENDOSCOPY; CLASSIFICATION ALGORITHM; COMPUTER PROGRAM; DISCRIMINANT ANALYSIS; EARLY DIAGNOSIS; GASTROINTESTINAL TRACT; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE SEGMENTATION; IMAGING AND DISPLAY; INTESTINAL BLEEDING; INTESTINE ULCER; KERNEL METHOD; SUPPORT VECTOR MACHINE; VECTOR SUPPORTED CONVEX CLASSIFICATION ALGORITHM; VIDEOENDOSCOPY;

CAPSULE ENDOSCOPY; FEATURE SELECTION; MEDICAL IMAGE ANALYSIS; TEXTURE ANALYSIS;

CAPSULE ENDOSCOPY; COLOR; EDUCATION, MEDICAL, CONTINUING;

EID: 84887825870     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2012.09.004     Document Type: Article

References (42)

1. Scapa E., Jacob H., Lewkowicz S., Migdal M., Gat D., Gluckhovski A., Gutmann N., Fireman Z. Initial experience of wireless-capsule endoscopy for evaluating occult gastrointestinal bleeding and suspected small bowel pathologyclinical use of wireless-capsule video endoscope. The American Journal of Gastroenterology 2002, 97:2776-2779.
2. Lewis B., Swain P. Capsule endoscopy in the evaluation of patients with suspected small intestinal bleeding: results of a pilot study. Gastrointestinal Endoscopy 2002, 56:349-353.
3. Adler D., Goustout C. Wireless capsule endoscopy. Hosp Physician 2003, 39:14-22.
4. Swain P. Wireless capsule endoscopy and Crohn's disease. GUT 2005, 54:323-326.
5. Wartel F., Maunoury V., Bulois P., Papadopoulos S., Filoche B., Colombel J.F. Smallbowel ulcerations at wireless capsule endoscopy: go the whole way. GUT 2007, 56:1132.
6. Chait M.M. Gastroesophageal reflux disease: important considerations for the older patients. World Journal of Gastrointestinal Endoscopy 2010, 2:388-396.
7. Lukashok H.P., Carlos Robles-Jara, Carlos Robles-Medranda Multiple Intestinal Erosions as a Result of Hemorrhage due to Parasites: Case Reports and Review of the Literature. Diagnostic and Therapeutic Endoscopy 2011, 2011. Article ID 340869, 3 pages. 10.1155/2011/340869.
8. Goenka M.K., Majumder S., Kumar S., Sethy P.K., Goenka U. Single center experience of capsule endoscopy in patients with obscure gastrointestinal bleeding. World Journal of Gastroenterology 2011, 17:774-778.
9. Berens J., Mackiewicz M.W., Bell G.D. Stomach, intestine, and colon tissue discriminators for wireless capsule endoscopy images. Medical Imaging 2005: Image Processing 2005, 283-290.
10. M. Coimbra, P. Campos, J. Cunha, Topographic segmentation and transit time estimation for endoscopic capsule exams, in: IEEE ICASSP, volume 2, Toulouse-France, pp. 1164-1167, 2006.
11. Coimbra M., Campos P., Cunha J. Topographic segmentation and transit time estimation for endoscopic capsule exams. IEEE ICASSP, volume 2 2006, 1164-1167.
12. Mackiewicz M., Berens J., Fisher M. Wireless capsule endoscopy color video segmentation. IEEE Transactions on Medical Imaging 2008, 27:1769-1781.
13. Vilarino F., Kuncheva L.I., Radeva P. Roc curves and video analysis optimization in intestinal capsule endoscopy. Pattern Recognition Letters 2006, 27:875-881.
14. Szczypiński P.M., Sriram R.D., Sriram P.V., Reddy D.N. A model of deformable rings for interpretation of wireless capsule endoscopic videos. Medical Image Analysis 2009, 13:312-324.
15. Bourbakis N. Detecting abnormal patterns in wce images. Proc. 5th IEEE Symposium on Bioninformatics and Bioengineering 2005.
16. Kodogiannis V., Boulougoura M., Lygouras J., Petrounias I. A neuro-fuzzy-based system for detecting abnormal patterns in wireless-capsule endoscopic images. Neurocomputing 2007, 70:704-717.
17. Barbosa D.J.C., Ramos J., Lima C.S. Detection of small bowel tumors in capsule endoscopy frames using texture analysis based on the discrete wavelet transform. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2008, 3012-3015.
18. Li B., Meng M.-H. Ulcer recognition in capsule endoscopy images by texture features. Proceedings of the 7th World Congress on Intelligent Control and Automation 2008, 234-239.
19. Mackiewicz M.W., Fisher M., Jamieson C. Bleeding detection in wireless capsule endoscopy using adaptive colour histogram model and support vector classification. Medical Imaging 2008: Image Processing 2008, 69140R-12.
20. Li B., Meng M. Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments. Computers in Biology and Medicine 2009, 39:141-147.
21. Szczypiński P., Klepaczko A. Convex hull-based feature selection in application to classification of wireless capsule endoscopic images. Advanced Concepts for Intelligent Vision Systems, volume 5807 of Lecture Notes in Computer Science 2009, 664-675. Springer, Berlin/Heidelberg. J. Blanc-Talon, W. Philips, D. Popescu, P. Scheunders (Eds.).
22. S. Hwang, M. Celebi, Polyp detection in wireless capsule endoscopy videos based on image segmentation and geometric feature, in: Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 678-681.
23. Charisis V., Hadjileontiadis L., Liastos C., Mavrogiannis C., Sergiadis G. Capsule endoscopy image analysis using texture information from various colour models. Computer Methods and Programs in Biomedicine 2011, 10.1016/j.cmpb.2011.10.004.
24. Barbosa D., Roupar D., Ramos J., Tavares A., Lima C. Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images. Biomedical engineering online 2012, 11:3.
25. Siedlecki W., Sklansky J. On automatic feature selection. International Journal of Pattern Recognition and Artificial Intelligence 1988, 2:197-220.
26. Pudil P., Novovicova J., Kittler J. Floating search methods in feature selection. Pattern Recognition Letters 1994, 15:1119-1125.
27. Kohavi R., John G. Wrappers for feature subset selection. Artificial Intelligence 1997, 97:273-324.
28. Oh I., Lee J., Moon B. Hybrid genetic algorithms for feature selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26:1424-1437.
29. Dy J., Brodley C. Feature selection for unsupervised learning. Journal of Machine Learning Research 2004, 5:845-889.
30. Klepaczko A., Materka A. Clustering-stability based feature selection for unsupervised texture classification. Machine Graphics and Vision 2009, 18:125-141.
31. Coimbra M., Campos P., Cunha J.P.S. Extracting clinical information from endoscopic capsule exams using mpeg-7 visual descriptors. IEE Seminar Digests 2005, 2005:105-110.
32. Coimbra M., Cunha J. Mpeg-7 visual descriptors-contributions for automated feature extraction in capsule endoscopy. IEEE Transactions on Circuits and Systems for Video Technology 2006, 16:628-637.
33. Giritharan B., Yuan X., Liu J., Buckles B., Oh J.-H., Tang S.J. Bleeding detection from capsule endoscopy videos. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2008, 4780-4783.
34. J.M. Martí nez, Mpeg-7 Overview, 2004, http://mpeg.chiariglione.org/standards/mpeg-7/mpeg-7.htm.
35. Szczypinski P., Strzelecki M., Materka A., Klepaczko A. Mazda-a software package for image texture analysis. Computer Methods and Programs in Biomedicine 2009, 94:66-76.
36. Klepaczko A., Kociński M., Materka A. Quantitative description of 3D vascularity images: texture-based approach and its verification thhrough cluster analysis. Pattern Analysis and Applications 2011, 14:415-424.
37. Blum A.L., Langley P. Selection of relevant features and examples in machine learning. Artificial Intelligence 1997, 97:245-271.
38. Vapnik V. The Nature of Statistical Learning Theory 1995, Springer-Verlag, New York.
39. Freedman D.A. Statistical Models: Theory and Practice 2005, Cambridge University Press.
40. Pudil P., Somol P. Current feature selection techniques in statistical pattern recognition. Computer Recognition Systems. Advances in Soft Computing 2005, Springer. M. Kurzynski, E. Puchala, M. Wozniak, A. Zolnierek (Eds.).
41. Barber C., Dobkin D., Huhdanpaa H. The Quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software 1996, 22:469-483.
42. Witten I.H., Frank E. Data Mining. Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann, San Francisco.