Tumor recognition in endoscopic video images using artificial neural network architectures

Conference Proceedings of the EUROMICRO

Volumn 2, Issue , 2000, Pages 423-429

  Karkanis, S A ^a   Iakovidis, D K ^a   Maroulis, D E ^a   Magoulas, G D ^b   Theofanous, N G ^a  

^a UNIVERSITY OF ATHENS   (Greece)

^b BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ENDOSCOPIC VIDEO IMAGES; MULTI-LAYER FEED FORWARD; ORIGINAL IMAGES; RECOGNITION SYSTEMS; STATISTICAL MEASURES; TEXTURE INFORMATION; TUMOR RECOGNITION; VIDEO FRAME;

NEURAL NETWORKS;

TUMORS;

EID: 84889237353     PISSN: 10896503     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/EURMIC.2000.874524     Document Type: Conference Paper

References (17)

1. Delaney, PM, Papworth, GD, King, RG. Fibre optic confocal imaging (FOCI) for in vivo subsurface microscopy of the colon. In: Preedy VR and Watson RR, eds. Methods in disease: Investigating the Gastrointestinal Tract. London: Greenwich Medical Media, 1998.
2. Haralick, R. M., Shanmugam, K. andDinstein, I. (1973). Textural Features for Image Classification IEEE Trans. Systems, Man and Cybernetics, 3, 6, 610-621.
3. Gotlieb C.C., Kreyszig Texture descriptors based on cooccurrence matrices. Comp. Vision, Graph, and Image Proc. 1990;51:70-86.
4. Haralick, R. M. (1979). Statistical and structural approaches to texture. IEEE Proceedings, 67, 786-804.
5. Durbin R, Miall C, Mitchison G. The Computing Neuron. Reading: Addison-Wesley, 1989.
6. Coppini G, Poli R, Valli G. Recovery of the 3-D shape of the left ventricle from echocardiographic images. IEEE Transactions on Medical Imaging 1995; 14: 301-317.
7. Hanka R, Harte TP, Dixon AK, Lomas DJ, Britton PD. Neural networks in the interpretation of contrast-enhanced magnetic resonance images of the breast. In: Proceedings of Healthcare Computing. Harrogate: UK, 1996: 275-283.
8. Ifeachor EC, Rosen KG, eds. Proceedings of the International Conference on Neural Networks and Expert Systems in Medicine and Healthcare. Plymouth: UK, 1994.
9. Innocent PR, Barnes M, John R. Application of the fuzzy ART/MAP and MinMax/MAP neural network models to radiographic image classification. Artif Intel!, in Med. 1997; 11:241-263.
10. Karkanis S, Magoulas GD, Grigoriadou M, Schurr M. Detecting abnormalities in colonoscopic images by textural description and neural networks. In: Proc. of Work, on Mach. Learn, in Med. Appl, Advance Course in Artif. Intell.-ACAI99. Chania: Greece, 1999: 59-62.
11. Lim CP, Harrison RF, Kennedy RL. Application of autonomous neural network systems to medical pattern classification tasks. Artificial Intelligence in Medicine 1997; 11:215-239.
12. Miller AS, Blott BH, Hames TK. Review of neural network applications in medical imaging and signal processing. Medical and Biological Engineering and Computing 1992; 30: 449-64.
13. Phee SJ, Ng WS, Chen IM, Seow-Choen F, Davies BL. Automation of colonoscopy part II: visual-control aspects. IEEE Engineering in Medicine and Biology May/June 1998: 81-88.
14. Reategui EB, Campbell JA, Leao BF. Combining a neural network with case-based reasoning in a diagnostic system. Artificial Intelligence in Medicine 1996; 9: 5-27.
15. Veropoulos K, Campbell C, Learmonth G. Image processing and neural computing used in the diagnosis of tuberculosis. In: Colloq. Intelligent Meth. in Health, and Med. Appl. York: UK, 1998.
16. Zhu Y, Yan H. Computerized tumor boundary detection using a Hopfield neural network. IEEE Tr. on Medical Imaging 1997; 16: 55-67.
17. Meyer Y., Wavelets: Algorithms and Applications, Philadelphia: SIAM, 1993.