Abstract
The high incidence of brain disease, especially brain tumor, has increased significantly in recent years. It is becoming more and more concernful to discover knowledge through mining medical brain image to aid doctors’ diagnosis. In this paper, we introduce a notion of image sequence similarity patterns (ISSP) for medical image database. These patterns are significant in medical images because it is the similarity of objects each of which has an image sequence that is meaningful. We design the new algorithms with the guidance of the domain knowledge to discover ISSP for similarity retrieval. Our experiments demonstrate that the results of similarity retrieval are satisfying.
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References
Hsu, W., Lee, M.L., Zhang, J.: Image Mining: Trends and Developments. Journal of Intelligent Information Systems 19(1), 7–23 (2002)
Zaiane, O.R., Antonie, M.-L., Coman, A.: Mammography Classification by an Association Rule-based Classifier. In: Proceedings of the Third International Workshop on Multimedia Data Mining (MDM/KDD 2002) (2002)
Kitamoto, A.: Data Mining for Typhoon Image Collection. In: Second International Workshop on Multimedia Data Mining (MDM/KDD 2001) (2001)
Ordonez, C., Omiecinski, E.: Discovering Association Rules Based on Image Content. In: IEEE Advances in Digital Libraries Conference (1999)
Haiwei, P., Li, J., Wei, Z., Fang, F.: Detecting Brain Tumor Based on Pixel’s Clustering. In: 17th International EURASIP Conference BIOSIGNAL (2004)
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© 2006 Springer-Verlag Berlin Heidelberg
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Haiwei, P., Xie, X., Wei, Z., Li, J. (2006). Mining Image Sequence Similarity Patterns in Brain Images. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_115
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DOI: https://doi.org/10.1007/978-3-540-36668-3_115
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36667-6
Online ISBN: 978-3-540-36668-3
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