Abstract
This paper presents an intelligent medical chromatic image understanding system for lung cancer cell identification based on fuzzy knowledge representation and reasoning. Following image analysis and a low-level feature extraction process, a two-layer rule-based fuzzy knowledge model is proposed to represent the domain knowledge needed for image understanding task. Experimental results show that the system achieves not only a high rate of overall correct identification, but also a low rate of false negative identification, that is, a low rate of identifying cancer cases to be normal ones, which is important in reducing false diagnosis cases.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ye, Y.K., Shao, C., Ge, X.Z., et al.: Design and Clinical research of a Novel Instrument for Lung Cancer Early-stage Diagnosis. Chinese Journal of Surgery 30(5), 303–305 (1992)
Bezdek, J.C., Hall, L.O., Clarke, L.P.: Review of MR image segmentation techniques using pattern recognition. Medical Physics 20(4), 1033–1048 (1993)
Kummert, F., et al.: Control and explanation in a signal understanding environment. Signal Processing 32, 111–145 (1993)
Tombropoulos, R., et al.: A Decision Aid for Diagnosis of Liver Lesions on MRI. Section on Medical Informatics, Stanford University School of Medicine, AMIA (1994)
Osareh, A., Mirmehdi, M., Thomas, B., Markham, R.: Automatic recognition of exudative maculopathy using fuzzy c-means clustering and neural networks. In: Medical Image Underestanding and Analysis Conference, pp. 49–52. BMVA Press (2001)
Grimson, W.: Medical Applications of Image Understanding. IEEE Expert 10, 18–28 (1995)
Yang, Y.B., Li, N., Chen, S.F., Chen, Z.Q.: Lung Cancer Identification Based on Image Content. In: 6th International Conference for Young Computer Scientists, vol. 1, pp. 237–240 (2001)
Freeman, H.: Computer Processing of Line-drawing Image. Computing Surveys 6(1), 57–97 (1974)
Section of Pathology, Shanghai Tumor Hospital: Applied Tumor Cytology. Shanghai, Shanghai People’s Publishing House (1975)
Baruch, O., Loew, M.H.: Segmentation of Two-dimensional Boundaries Using the Chain Code. Pattern Recognition 21(6), 581–589 (1988)
Gaurav, S.: Digital Color Imaging. IEEE Transactions On Image Processing 6(7), 901–932 (1997)
Mehtre, B.M., Kankanhalli, M.S., et al.: Color Matching for Image Retrieval. Pattern Recognition Letters 16(3), 325–331 (1995)
Zadeh, L.A.: Fuzzy Logic and its Application to Approximate Reasoning. Information Processing 74, 591–594 (1974)
Terano, T., Asai, K., Sugeno, M.: Fuzzy System Theory and its Applications. Academic Press, Boston (1992)
Kuncheva, L.I.: How Good Are fuzzy If-then Classiers? IEEE Transactions on Systems, Man, and Cybernetics 30(4), 501–509 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, Y., Chen, S., Lin, H., Ye, Y. (2004). A Chromatic Image Understanding System for Lung Cancer Cell Identification Based on Fuzzy Knowledge. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_41
Download citation
DOI: https://doi.org/10.1007/978-3-540-24677-0_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22007-7
Online ISBN: 978-3-540-24677-0
eBook Packages: Springer Book Archive