An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy

Computers in Biology and Medicine

Volumn 36, Issue 10, 2006, Pages 1084-1103

  Iakovidis, Dimitris K ^a   Maroulis, Dimitris E ^a   Karkanis, Stavros A ^b  

^a UNIVERSITY OF ATHENS   (Greece)

^b TECHNOLOGICAL EDUCATIONAL INSTITUTE OF LAMIA   (Greece)

Author keywords

Adenomas; Biomedical system; Color texture features; Gastrointestinal lesions; Support vector machines; Video endoscopy

Indexed keywords

BIOMEDICAL SYSTEM; COLOR-TEXTURE FEATURES; GASTROINTESTINAL LESIONS; SUPPORT VECTOR MACHINES; VIDEO ENDOSCOPY;

BIOMEDICAL ENGINEERING; COMPUTATIONAL METHODS; DISEASES; ENDOSCOPY; FEATURE EXTRACTION; VIDEO SIGNAL PROCESSING;

INTELLIGENT AGENTS;

ACCURACY; ARTICLE; GASTROINTESTINAL ADENOMA; GASTROINTESTINAL ENDOSCOPY; GASTROINTESTINAL TUMOR; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; TELEMEDICINE; TUMOR DIAGNOSIS; VIDEORECORDING;

ADENOMA; ARTIFICIAL INTELLIGENCE; COLONIC POLYPS; DECISION MAKING, COMPUTER-ASSISTED; DIAGNOSIS, COMPUTER-ASSISTED; ENDOSCOPES, GASTROINTESTINAL; EXPERT SYSTEMS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MICROCOMPUTERS; POLYPS; SOFTWARE; STOMACH NEOPLASMS; VIDEO RECORDING;

EID: 33746682430     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2005.09.008     Document Type: Article

References (50)

1. S.H. Itzkowitz, Y.S. Kim, Sleisinger & Fordtran's Gastrointestinal and Liver Disease, sixth ed., vol. 2, WB Saunders Company, Philadelphia, 1998.
2. Johnson C.D., and Dachman A.H. CT colonography: the next colon screening examination. Radiology 216 (2000) 331-341
3. Rex D., Weddle R., Pound D., O'Connor K., Hawes R., Dittus R., Lappas J., and Lumeng L. Flexible sigmoidoscopy plus air contrast barium enema versus colonoscopy for suspected lower gastrointestinal bleeding. Gastroenterology 98 (1990) 855-861
4. H. Kodama, F. Yano, S.P. Ninomija, Y. Sakai, S. Ninomija, A digital imaging processing method for gastric endoscope picture, in: Proceedings of the 21st Annual Hawaian International Conference on System Sciences, vol. 4, 1988, pp. 277-282.
5. S.M. Krishnan, C.S. Tan, K.L. Chan, Closed-boundary extraction of large intestinal lumen, in: Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, 1994, pp. 610-611.
6. S.M. Krishnan, X. Yang, K.L. Chan, S. Kumar, P.M.Y. Goh, Intestinal abnormality detection from endoscopic images, in: Proceedings of the International Conference of the IEEE on Engineering in Medicine and Biology Society, vol. 2, 1998, pp. 895-898.
7. Esgiar A.N., Naguib R.N.G., Sharif B.S., Bennett M.K., and Murray A. Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa. IEEE Trans. Inform. Technol. Biomed. 2 (1998) 197-203
8. Kudo S., Tamura S., Nakajima T., Yamano H., Kusaka H., and Watanabe H. Diagnosis of colorectal tumorous lesions by magnifying endoscopy. Gastrointest. Endos. 44 (1996) 8-14
9. S. Karkanis, K. Galousi, D. Maroulis, Classification of endoscopic images based on texture spectrum, in: Proceedings of the Workshop on Machine Learning in Medical Applications, Advance Course in Artificial Intelligence, 1999, pp. 63-69.
10. S. Karkanis, G.D. Magoulas, M. Grigoriadou, M. Schurr, Detecting abnormalities in colonoscopic images by textural description and neural networks, in: Proceedings of the Workshop on Machine Learning in Medical Applications, Advance Course in Artificial Intelligence, Chania, Greece, 1999, pp. 59-62.
11. Magoulas G.D., Plagianakos V.P., and Vrahatis M.N. Neural network-based colonoscopic diagnosis using on-line learning and differential evolution. Appl. Soft Comput. 4 (2004) 369-379
12. P. Wang, S.M. Krishnan, C. Kugean, M.P. Tjoa, Classification of endoscopic images based on texture and neural network, in: Proceedings of the 23rd IEEE Engineering in Medicine and Biology, vol. 4, 2001, pp. 3691-3695.
13. Karkanis S.A., Magoulas G.D., Iakovidis D.K., Karras D.A., and Maroulis D.E. Evaluation of textural feature extraction schemes for neural network-based interpretation of regions in medical images. Proceedings of the IEEE International Conference on Image Processing (2001), Thessaloniki, Greece 281-284
14. Karkanis S.A., Iakovidis D.K., Karras D.A., and Maroulis D.E. Detection of lesions in endoscopic video using textural descriptors on wavelet domain supported by artificial neural network architectures. Proceedings of the IEEE International Conference on Image Processing (2001), Thessaloniki, Greece 833-836
15. Maroulis D.E., Iakovidis D.K., Karkanis S.A., and Karras D.A. CoLD: A versatile system for detection of colorectal lesions in endoscopy video-frames. Comput. Methods Programs Biomed. 70 (2003) 151-166
16. M.P. Tjoa, S.M. Krishnan, Feature extraction for the analysis of colon status from the endoscopic images, BioMedical Engineering OnLine, 2003, pp. 2-9.
17. Karkanis S.A., Iakovidis D.K., Maroulis D.E., Karras D.A., and Tzivras M. Computer aided tumor detection in endoscopic video using color wavelet features. IEEE Trans. Inform. Technol. Biomed. 7 (2003) 141-152
18. Van de Wouwer G., Scheunders P., Livens S., and Van Dyck D. Wavelet correlation signatures for color texture characterization. Pattern Recognition 32 (1999) 443-451
19. Zheng M.M., Krishnan S.M., and Tjoa M.P. A fusion-based clinical decision support for disease diagnosis from endoscopic images. Comput. Biol. Med. 35 (2005) 259-274
20. Swets J.A., Dawes R.M., and Monahan J. Psychological science can improve diagnostic decisions. Psychol. Sci. Public Interest 1 (2000) 1-26
21. Y. Miyake, N. Tsumura, M. Takeya, R. Inagawa, Digital color images in biomedicine, in: H. Tanaka, Y. Miyake, M. Nishibori, D. Mukhopadhyay (Eds.), Japan, 2001.
22. Shiobara T., Haneishi H., and Miyake Y. Improved color reproduction of electronic endoscopes. J. Imaging Sci. Technol. 40 (1996) 494-501
23. Wyszecki G., and Styles W.S. Color Science: Concepts and Methods Quantitative Data and Formulae (1982), Wiley, New York
24. Y. Ohta, T. Kanade, T. Sakai, Color information for region segmentation, in: Proceedings on Computer Graphics and Image Processing, vol. 13, 1980, pp. 222-241.
25. Paschos G. Perceptually uniform color spaces for color texture analysis: an empirical evaluation. IEEE Trans. Image Process. 10 6 (2001) 932-937
26. Theodoridis S., and Koutroumbas K. Pattern Recognition (1999), Academic Press, San Diego
27. Unser M. Texture classification and segmentation using wavelet frames. IEEE Trans. Image Process. 4 (1995) 1549-1560
28. Randen T., and Husøy J.H. Filtering for texture classification: a comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 21 (1999) 291-310
29. Drimbarean A., and Whelan P.F. Experiments in colour texture analysis. Pattern Recognition Lett. 22 (2001) 1161-1167
30. T. Mäenpää, M. Pietikäinen, J. Viertola, Separating color and pattern information for color texture discrimination, Proceedings of the 16th International Conference on Pattern Recognition, Quebec City, Canada, 2002, pp. 668-671.
31. D.K. Iakovidis, D.E. Maroulis, S.A. Karkanis, I.N. Flaounas, Color texture recognition in video sequences using wavelet covariance features and support vector machines, in: Proceedings of the 29th EUROMICRO Conference, Antalya, Turkey, 2003, pp. 199-204.
32. D.K. Iakovidis, S.A. Karkanis, D.E. Maroulis, Color wavelet features for texture recognition under varying illumination using support vector machines, in: Proceedings of the IEEE International Conference on Image Processing, Singapore, 2004, pp. 1505-1508.
33. Mallat S.G. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11 (1989) 674-693
34. W.Y. Ma, B.S. Manjunath, A comparison of wavelet transform features for texture image annotation, Int. Conf. Image Process, 1995, pp. 256-259.
35. Mojscilovic A., Popovic M.V., and Rackov D.M. On the selection of an optimal wavelet basis for texture characterization. IEEE Trans. Image Processing 9 (2000) 2043-2050
36. Haralick R.M., Shanmugam K., and Dinstein I. Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3 (1973) 610-621
37. Ojala T., Pietikäinen M., and Harwood D. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29 (1996) 51-99
38. He D.C., and Wang L. Texture unit, texture spectrum, and texture analysis. IEEE Trans. Geosci. Remote Sensing 28 (1990) 509-512
39. Pietikäinen M., and Ojala T. Nonparametric texture analysis with complementary spatial operator. In: Pietikäinen M. (Ed). Texture Analysis in Machine Vision, Series in Machine Perception and Artificial Intelligence vol. 40 (2000), World Scientific, Singapore 3-18
40. West D., and West V. Model selection for a medical diagnostic decision support system: a breast cancer detection case. Artif. Intell. Med. 20 (2000) 183-204
41. Papik K., Molnal B., Schaefer R., Dombovari Z., Tulassay Z., and Feher J. Application of neural networks in medicine-a review. Diagnostics Med. Technol. 4 3 (1998) 538-546
42. Vapnik V. Statistical Learning Theory (1998), Wiley, New York
43. Burges C. A Tutorial on Support Vector Machines for Pattern Recognition (1998), Kluwer Academic Publishers, Boston
44. J. Huang, X. Shao, H. Wechsler, Face pose discrimination using support vector machines (SVM), in: Proceedings of the ICPR International Conference on Pattern Recognition, vol. 1, 1998, pp. 154-156.
45. El-Naqa I., Yongyi Y., Wernick M.N., Galatsanos N.P., and Nishikawa R.M. A support vector machine approach for detection of microcalcifications. IEEE Trans. Med. Imaging 21 12 (2002) 1552-1563
46. Li S., Kwok J.T., Zhu H., and Wand Y. Texture classification using the support vector machines. Pattern Recognition 36 (2003) 2883-2893
47. Gokturk S.B., Tomasi C., Acar B., Beaulieu C.F., Paik D.S., Jeffrey Jr. R.B., Yee J., and Napel S. A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography. IEEE Trans. Med. Imaging 20 12 (2001) 1251-1260
48. G.M. Weiss, F. Provost, The effect of class distribution on classifier learning, Technical Report ML-TR-43, Department of Computer Science, Rudgers University, January 2001.
49. Bariamis D., Iakovidis D.K., Maroulis D.E., and Karkanis S.K. An FPGA-based Architecture for Real Time Image Feature Extraction. Proceedings of the ICPR International Conference on Pattern Recognition (2004), Cambridge, UK 801-804
50. Anguita D., Boni A., and Ridella S. A digital architecture for support vector machines: theory, algorithm, and FPGA implementation. IEEE Trans. Neural Networks 14 5 (2003) 993-1009