Medical Image Retrieval Based on Semi-Supervised Learning

Hui Liu; Cai Ming Zhang; Hua Han

doi:10.4028/www.scientific.net/AMR.108-111.201

Paper Titles

Research on Web-Oriented Detection and Early-Warning of Evolutional Public Emergencies
p.178

A New Method on Laboratory Evaluation of the Effectiveness of Consolidant Treatments for the Conservation of Historic Stone Buildings and Sculptures
p.184

Study of Relative Radiometric Normalization Based on Multitemporal ASTER Images
p.190

Study on Enterprise Knowledge Management Based on Knowledge Grid
p.195

Medical Image Retrieval Based on Semi-Supervised Learning
p.201

Study on Dynamic Load Balance Method Based on Genetic Algorithm and RBF Neural Network
p.207

Research on Information Resources Management System Based on Domain Ontology
p.211

Personalized Intelligent Information Retrieval Entrance Mechanism
p.216

Study on Web Information Intelligent Extraction for Agricultural Product Quantity Security System
p.222

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 108-111Medical Image Retrieval Based on Semi-Supervised...

Medical Image Retrieval Based on Semi-Supervised Learning

Abstract:

Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 108-111)

Pages:

201-206

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.108-111.201

Citation:

Cite this paper

Online since:

May 2010

Authors:

Hui Liu, Cai Ming Zhang, Hua Han

Keywords:

Co-Training, Image Retrieval, Medical Image Character, Semi-Supervised Learning, Unlabeled Data

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] ZHI-HUA ZHOU, KE-JIA CHEN, and HONG-BIN DAI, Enhancing Relevance Feedback in Image Retrieval Using Unlabeled Data, ACM Journal Name, Vol. TBD, No. TBD, 2006, pp.1-25.

Google Scholar

[2] S. Tong and E. Chang. Support vector machine active learning for image retrieval. In Proc. ACM Multimedia Conference, 2001. 1, 2, 5, 6.

DOI: 10.1145/500141.500159

Google Scholar

[3] A. Blum, T. Mitchell. Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory (COLT'98), Wisconsin, MI, 1998, pp.92-100.

DOI: 10.1145/279943.279962

Google Scholar

[4] K. Nigam, R. Ghani. Analyzing the effectiveness and applicability of co-training. In: Proceedings of the 9th ACM International Conference on Information and Knowledge Management (CIKM'00), McLean, VA, 2000, pp.86-93.

DOI: 10.1145/354756.354805

Google Scholar

[5] S. Goldman, Y. Zhou. Enhancing supervised learning with unlabeled data. In: Proceedings of the 17th International Conference on Machine Learning (ICML'00), San Francisco, CA, 2000, pp.327-334.

Google Scholar

[6] Zhou, Z. -H. and Li, M. Tri-training: exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17, 1529-1541.

DOI: 10.1109/tkde.2005.186

Google Scholar

[7] A. H. Mir, M. Hanmandlu, S. N. Tandon, . Texture analysis of CT images, IEEE Eng. Med. Biol. Mag., vol. 14, pp.781-786, (1995).

DOI: 10.1109/51.473275

Google Scholar

[8] B. Van Ginneken, B. M. Ter Haar Romeny, M. A. Viergever,. Computer-aided diagnosis in chest radiography: a survey., IEEE Trans. Med. Imag , vol 20, no. 12 , pp.1228-1241, Dec. (2001).

DOI: 10.1109/42.974918

Google Scholar

[9] T. Joachims, Transductive inference for text classification using support vector machines, in: Proceedings of the 16th International Conference on Machine Learning, San Francisco, USA, 1999, pp.200-209.

DOI: 10.1007/978-1-4615-0907-3_7

Google Scholar

[10] Muller, H., Muller, W., Squire, D. M., Marchand-Maillet, S., and Pun, T. 2001. Performance evaluation in content-based image retrieval: Overview and proposals. Pattern Recognition Letters 22, 5, pp.593-601.

DOI: 10.1016/s0167-8655(00)00118-5

Google Scholar

[11] Lewis, D. Representation and learning in information retrieval. Ph.D. thesis, Department of Computer Science, University of Massachusetts, Amherst, MA. (1992).

Google Scholar