INFORMATION CHANGE THE WORLD

International Journal of Information Engineering and Electronic Business(IJIEEB)

ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)

Published By: MECS Press

IJIEEB Vol.3, No.3, Jun. 2011

A Domain Knowledge Based Approach for Medical Image Retrieval

Full Text (PDF, 204KB), PP.16-22


Views:76   Downloads:1

Author(s)

Haiwei Pan,Xiaolei Tan,Qilong Han,Guisheng Yin

Index Terms

Data mining,image mining,similarity retrieval,domain knowledge

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. Image mining is the important branch of data mining. It is more than just an extension of data mining to image domain but an interdisciplinary endeavor. Image clustering and similarity retrieval are two basilic parts of image mining. In this paper, we introduce a notion of image sequence similarity patterns (ISSP) for medical image database. ISSP refer to the longest similar and continuous sub-patterns hidden in two objects each of which contains an image sequence. These patterns are significant in medical images because the similarity for two medical images is not important, but rather, 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 the possible Space-Occupying Lesion (PSO) in brain images and ISSP for similarity retrieval. Our experiments demonstrate that the results of similarity retrieval are meaningful and interesting to medical doctors.

Cite This Paper

Haiwei Pan,Xiaolei Tan,Qilong Han,Guisheng Yin,"A Domain Knowledge Based Approach for Medical Image Retrieval", IJIEEB, vol.3, no.3, pp.16-22, 2011.

Reference

[1]Zaiane, O.R. et al. (1998). Mining MultiMedia Data. CASCON: Meeting of Minds.

[2]WYNNE HSU, MONG LI LEE, JI ZHANG. Image Mining: Trends and Developments. Journal of Intelligent Information Systems, 19:1, 7–23, 2002.

[3]Vasileios Megalooikonomou, Christos Davatzikos, Edward H. Herskovits. Mining Lesion-Deficit Associations in a Brain Image Database. KDD-99 San Diego CA USA.

[4]Wynne Hsu, Mong Li Lee, Kheng Guan Goh. Image Mining in IRIS: Integrated Retinal Information System. Proceedings of the ACM SIGMOD, May 2000, Dellas, Texas, U.S.A., pp. 593.

[5]Y. Liu, F. Dellaert, W.E. Rothfus, A. Moore, J. Schneider, and T. Kanade. Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures. Proceedings of the Medical Imaging Computing and Computer Assisted Intervention Conference (MICCAI 2001), Utrecht, The Netherlands, October, 2001.

[6]Maria-Luiza Antonie, Osmar R. Zaiane, Alexandru Coman. Application of Data Mining Techniques for Medical Image Classification. Proceedings of the Second International Workshop on Multimedia Data Mining (MDM/KDD'2001).

[7]Osmar R. Zaiane, Maria-Luiza Antonie, Alexandru Coman. Mammography Classification by an Association Rule-based Classifier. Proceedings of the Third International Workshop on Multimedia Data Mining (MDM/KDD'2002).

[8]Fayyad, U.M., Djorgovski, S.G., and Weir, N. (1996). Automating the Analysis and Cataloging of Sky Surveys. Advances in Knowledge Discovery and Data Mining, 471–493.

[9]Kitamoto, A. (2001). Data Mining for Typhoon Image Collection. In Second International Workshop on Multimedia Data Mining (MDM/KDD’2001).

[10]Ordonez, C. and Omiecinski, E. (1999). Discovering Association Rules Based on Image Content. In IEEE Advances in Digital Libraries Conference.

[11]Burl, MC et al. Mining For Image Content. In systems, Cybernetics, and Informatics / Information Systems: Analysis and Synthesis (1999).

[12]Soltanian-Zadeh H., Nezafat R., and Windham J.P.: “Is There Texture Information in Standard Brain MRI ?”, Proceedings of SPIE Medical Imaging 1999: Image Processing conference, San Diego, CA, Feb.1999.

[13]Barra V., Boire J-Y., “Tissue segmentation on MR Images of the Brain by Positivistic Clustering on a 3D Wavelet Representation.”, J. of Magnetic resonance Imaging, vol.11, pp. 267-278, 2000.