Automatic lesion detection in capsule endoscopy based on color saliency: Closer to an essential adjunct for reviewing software

Gastrointestinal Endoscopy

Volumn 80, Issue 5, 2014, Pages 877-883

  Iakovidis, Dimitris K ^a   Koulaouzidis, Anastasios ^b  

^a University of Central Greece   (Greece)

^b ROYAL INFIRMARY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; AUTOMATION; CAPSULE ENDOSCOPE; CAPSULE ENDOSCOPY; COLOR; DIAGNOSTIC IMAGING; GREECE; IMAGE ANALYSIS; IMAGING SOFTWARE; INTESTINE LYMPHANGIECTASIA; LYMPHANGIECTASIS; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; UNITED KINGDOM; VALIDITY; AUTOMATED PATTERN RECOGNITION; CASE CONTROL STUDY; COMPUTER ASSISTED DIAGNOSIS; COMPUTER PROGRAM; DUODENAL DISEASES; GASTROINTESTINAL HEMORRHAGE; HUMAN; ILEAL DISEASES; IMAGE PROCESSING; INFORMATION PROCESSING; INTESTINAL POLYPS; JEJUNAL DISEASES; LYMPHANGIECTASIS, INTESTINAL; PEPTIC ULCER; PROCEDURES; REPRODUCIBILITY; STOMATITIS, APHTHOUS;

AUTOMATIC DATA PROCESSING; CAPSULE ENDOSCOPY; CASE-CONTROL STUDIES; COLOR; DIAGNOSIS, COMPUTER-ASSISTED; DUODENAL DISEASES; GASTROINTESTINAL HEMORRHAGE; HUMANS; ILEAL DISEASES; IMAGE PROCESSING, COMPUTER-ASSISTED; INTESTINAL POLYPS; JEJUNAL DISEASES; LYMPHANGIECTASIS, INTESTINAL; PATTERN RECOGNITION, AUTOMATED; PEPTIC ULCER; REPRODUCIBILITY OF RESULTS; ROC CURVE; SOFTWARE; STOMATITIS, APHTHOUS;

EID: 84927799456     PISSN: 00165107     EISSN: 10976779     Source Type: Journal    
DOI: 10.1016/j.gie.2014.06.026     Document Type: Article

References (25)

1. Koulaouzidis A, Rondonotti E, Karargyris A. Small-bowel capsule endoscopy: A ten-point contemporary review. World J Gastroenterol 2013;19: 3726.
2. Lo SK. How should we do capsule reading? Tech Gastrointest Endosc 2006;8:146-8.
3. Iakovidis DK, Tsevas S, Polydorou A. Reduction of capsule endoscopy reading times by unsupervised image mining. Comput Med Imaging Graph 2010;34:471-8.
4. Kanaris I, Tsevas S, Maglogiannis I, Iakovidis DK. Enabling distributed summarization of wireless capsule endoscopy video. In: Imaging Systems and Techniques (IST), 2010 IEEE International Conference on: IEEE; 2010. p. 17-21.
5. Iakovidis D, Goudas T, Smailis C, et al. Ratsnake: A versatile image annotation tool with application to computer-aided diagnosis. Sci World J 2014;2014:286856.
6. Korman L, Delvaux M, Gay G, et al. Capsule endoscopy structured terminology (CEST): proposal of a standardized and structured terminology for reporting capsule endoscopy procedures. Endoscopy 2005;37: 951-9.
7. Wyszecki G, Stiles WS. Color science. Vol. 8. New York (NY): Wiley; 1982.
8. Bay H, Ess A, Tuytelaars T, et al. Speeded-up robust features (SURF). Comput Vision Image Understand 2008;110:346-59.
9. Tanaka M, Kidoh Y, Kamei M, et al. A new instrument for measurement of gastrointestinal mucosal color. Dig Endosc 1996;8:139-46.
10. Burges CJ. A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery 1998;2:121-67.
11. Li B, Meng MQH. Automatic polyp detection for wireless capsule endoscopy images. Expert Syst Applications 2012;39:10952-8.
12. Hwang S. Bag-of-visual-words approach to abnormal image detection in wireless capsule endoscopy videos. In: Advances in visual computing. New York (NY): Springer; 2011. p. 320-7.
13. Airola A, Pahikkala T, Waegeman W, et al. An experimental comparison of cross-validation techniques for estimating the area under the ROC curve. Comput Stat Data Anal 2011;55:1828-44.
14. Blanchet G, Charbit M. Digital signal and image processing using Matlab. Vol. 666. Hoboken (NJ): John Wiley & Sons; 2010.
15. Fawcett T. An introduction to ROC analysis. Pattern Recognition Lett 2006;27:861-74.
16. Li BP, Meng MQH. Comparison of several texture features for tumor detection in CE images. J Med Syst 2012;36:2463-9.
17. Zheng Y, Hawkins L, Wolff J, et al. Detection of lesions during capsule endoscopy: physician performance is disappointing. Am J Gastroenterol 2012;107:554-60.
18. Karkanis SA, Iakovidis DK, Karras D, et al. Detection of lesions in endoscopic video using textural descriptors on wavelet domain supported by artificial neural network architectures. 2001. Proceedings of the 2001 International Conference on Image Processing, vol. 2. IEEE; 2001. p. 833-6.
19. Li B, Meng MH. Computer-aided detection of bleeding regions for capsule endoscopy images. IEEE Trans Biomed Eng 2009;56:1032-9.
20. Lv G, Yan G, Wang Z. Bleeding detection in wireless capsule endoscopy images based on color invariants and spatial pyramids using support vector machines. In: Engineering in Medicine and Biology Society, 2011 Annual International Conference of the IEEE. IEEE; 2011. p. 6643-6.
21. Charisis VS, Katsimerou C, Hadjileontiadis LJ, et al. Computer-aided capsule endoscopy images evaluation based on color rotation and texture features: An educational tool to physicians. In: IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS). IEEE; 2013. p. 203-8.
22. Yu L, Yuen PC, Lai J. Ulcer detection in wireless capsule endoscopy images. In: 21st International Conference on Pattern Recognition (ICPR). IEEE; 2012. p. 45-8.
23. Kumar R, Zhao Q, Seshamani S, et al. Assessment of Crohn's disease lesions in wireless capsule endoscopy images. IEEE Trans Biomed Eng 2012;59:355-62.
24. Karargyris A, Bourbakis N. Detection of small bowel polyps and ulcers in wireless capsule endoscopy videos. IEEE Trans Biomed Eng 2011;58: 2777-86.
25. Szczypinski P, Klepaczko A, Pazurek M, et al. Texture and color based image segmentation and pathology detection in capsule endoscopy videos. Comput Methods Programs Biomed 2014;113:396-411.