Neuro-fuzzy Classification System for Wireless-Capsule Endoscopic Images

In this research study, an intelligent detection system to support medical diagnosis and detection of abnormal lesions by processing endoscopic images is presented. The images used in this study have been obtained using the M2A Swallowable Imaging Capsule - a patented, video color-imaging disposable capsule. Schemes have been developed to extract texture features from the fuzzy texture spectra in the chromatic and achromatic domains for a selected region of interest from each color component histogram of endoscopic images. The implementation of an advanced fuzzy inference neural network which combines fuzzy systems and artificial neural networks and the concept of fusion of multiple classifiers dedicated to specific feature parameters have been also adopted in this paper. The achieved high detection accuracy of the proposed system has provided thus an indication that such intelligent schemes could be used as a supplementary diagnostic tool in endoscopy.

Authors:

• Vassilis S. Kodogiannis
• John N. Lygouras

Keywords:

• Medical imaging
• Computer aided diagnosis
• Endoscopy
• Neuro-fuzzy networks
• Fuzzy integral.

References:

[1] K. Nagasako, T. Fujimori, Y. Hoshihara, M. Tabuchi, 1998. Atlas of
Gastroenterologic Endoscopy by High-resolution Video-Endoscopy,
IGAKU-SHOIN Ltd., Tokyo
[2] S. Krishnan, P. Wang, C. Kugean, M. Tjoa, "Classification of
endoscopic images based on texture and neural network", Proc. 23rd
Annual IEEE Int. Conf. in Engineering in Medicine and Biology, (4),
pp. 3691-3695, 2001
[3] D.E. Maroulis, D.K. Iakovidis, S.A. Karkanis, D.A. Karras, "CoLD: a
versatile detection system for colorectal lesions endoscopy videoframes",
Computer Methods and Programs in Biomedicine, (70), pp.
151-166, 2003
[4] A.F. Ravens, C.P. Swain, "The wireless capsule: new light in the
darkness", in Digestive Diseases, vol. 20, pp. 127-133, 2002.
[5] G. Idden, G. Meran, A. Glukhovsky anÔé¼d P. Swain, "Wireless capsule
endoscopy", Nature, pp. 405-417, 2000
[6] M. Mylonaki, A. Fritscher-Ravens, C.P. Swain, "Clinical results of
wireless capsule endoscopy" Gastrointest. Endosc., vol.55, AB146,
2002
[7] P. Spyridonos, F. Vilariño, J. Vitrià and P. Radeva, "Identification of
Intestinal Motility Events of Capsule Endoscopy Video Analysis", Proc.
of Advanced Concepts for Intelligent Vision Systems Conf, Antwerp,
Belgium, pp. 575-580, 2005
[8] F. Vilariño, P. Spyridonos, J. Vitrià, P. Radeva, "Self Organized Maps
for Intestinal Contractions Categorization with Wireless Capsule Video
Endoscopy", Proc. of the 3rd European Medical and Biological
Engineering Conference, EMBEC'05 Prague, pp. 3443-3447, 2005.
[9] N. Bourbakis, S. Makrogiannis, D. Kavraki, "A Neural Network-based
Detection of Bleeding in sequences of WCE images", Proc. of the 5th
IEEE Symposium on Bioinformatics and Bioengineering (BIBE-05),
pp. 324-327, 2005
[10] P.Y Lau, P.L. Correia, "Detection of bleeding patterns in WCE video
using multiple features", Engineering in Medicine and Biology Society,
EMBS 2007, 29th Annual International Conference of the IEEE, pp.
5601-5604, 2007
[11] M.T. Coimbra, J.P.S. Cunha, "MPEG-7 Visual Descriptors-
Contributions for Automated Feature Extraction in Capsule
Endoscopy", IEEE transactions on circuits and systems for video
technology, Vol. 16, No. 5, pp. 628-637, 2006.
[12] M. Gletsos, S. Mougiakakou, G. Matsopoulos, K. Nikita, A. Nikita, D.
Kelekis, "A computer-aided diagnostic system to characterize CT focal
liver lesions: design and optimization of a neural network classifier",
IEEE Trans. on Information Technology in Biomedicine, Vol. 7, No. 3,
pp. 153-162, 2003.
[13] D-C He, L. Wang, "Texture features based on texture spectrum",
Pattern Recognition, Vol. 24, No. 5, pp. 391-399, 1991.
[14] M. Boulougoura, E. Wadge, V.S. Kodogiannis, H.S. Chowdrey,
"Intelligent systems for computer-assisted clinical endoscopic image
analysis", 2nd IASTED Int. Conf. on Biomedical Engineering,
Innsbruck, Austria, pp. 405-408, 2004.
[15] V.S. Kodogiannis, M. Boulougoura, E. Wadge, J.N. Lygouras, "The
usage of soft-computing methodologies in interpreting capsule
endoscopy", Engineering Applications in Artificial Intelligence,
Elsevier 2007, Vol. 20, pp. 539-553.
[16] R. Haralick, "Statistical and structural approaches to texture", IEEE
Proc, Vol. 67, pp. 786- 804, 1979
[17] A. Barcelo, E. Montseny, P. Sobrevilla, Fuzzy Texture Unit and Fuzzy
Texture Spectrum for texture characterization, Fuzzy Sets and Systems,
Vol. 158, pp. 239 - 252, 2007
[18] V.S. Kodogiannis, "Intelligent classification of bacterial clinical isolates
in vitro, using an array of gas sensors", J. Intelligent and Fuzzy systems,
Vol. 16, No. 1, pp. 1-14, 2005
[19] Sugeno, M. "Fuzzy measures and fuzzy integrals: a survey, in Fuzzy
Automata and Decision Processes", (M.M. Gupta, G. N. Saridis, and
B.R. Gaines, editors), pp. 89-102, North-Holland, 1977
[20] R. Yager, "A General Approach to Criteria Aggregation using Fuzzy
Measures", International Journal of Man-Machine Studies, Vol. 39, No.
2, pp. 187-213, 1993.
[21] M. Grabisch, T. Murofushi, M. Sugeno, M., "Fuzzy measure of fuzzy
events defined by fuzzy integrals", Fuzzy Sets and Systems, Vol. 50,
pp. 293-313, 1992
[22] S. Mitra, S.K. Pal, P. Mitra, "Data mining in soft computing framework:
A survey", IEEE Transactions on Neural Networks, Vol. 13, No. 1, pp.
3-14, 2000
[23] L.I Kuncheva, Fuzzy Classifier Design, Physica-Verlag, 2000
[24] C.T. Teng Lin, C.S. George Lee, Neural Fuzzy Systems, Prentice Hall,
1996
[25] V. Kodogiannis, "Computer-aided Diagnosis in Clinical Endoscopy
using Neuro-Fuzzy System", in Proceedings of IEEE FUZZ 2004, pp.
1425-1429, 2004