Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

Classification of Brain Magnetic Resonance Images using 2 Level Decision Tree Learning

2 단계 결정트리 학습을 이용한 뇌 자기공명영상 분류

  • 김형일 (동국대학교 컴퓨터공학과) ;
  • 김용욱 (동국대학교 컴퓨터공학과)
  • Published : 2007.01.15
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper we present a system that classifies brain MR images by using 2 level decision tree learning. There are two kinds of information that can be obtained from images. One is the low-level features such as size, color, texture, and contour that can be acquired directly from the raw images, and the other is the high-level features such as existence of certain object, spatial relations between different parts that must be obtained through the interpretation of segmented images. Learning and classification should be performed based on the high-level features to classify images according to their semantic meaning. The proposed system applies decision tree learning to each level separately, and the high-level features are synthesized from the results of low-level classification. The experimental results with a set of brain MR images with tumor are discussed. Several experimental results that show the effectiveness of the proposed system are also presented.

본 논문에서는 뇌 자기공명영상을 분류하기 위하여 결정트리 알고리즘을 2 단계로 적용하는 영상 분류 시스템을 제안한다. 영상으로부터 얻을 수 있는 정보에는 두 종류가 있다. 하나는 크기, 색상, 질감, 윤곽선 등 영상으로부터 직접 얻을 수 있는 하위레벨 특징들이고, 다른 하나는 특정 객체의 존재 유무, 여러 부위 사이의 공간적 관계 등 분할된 영상들에 대한 해석을 통해서 얻을 수 있는 상위레벨 특징들이다. 의미에 따라 영상을 분류하기 위해서는 상위레벨 특징들을 기반으로 학습 및 분류가 수행되어야 한다. 제안하는 시스템에서는 결정트리 학습을 각각의 레벨에 개별적으로 적용하며, 하위레벨 분류 결과를 이용하여 상위레벨의 특징을 추출한다. 종양이 있는 뇌 자기공명영상 집합에 대하여 분류 실험을 수행하였으며, 몇 가지 실험 결과를 통해 제안된 시스템의 효과를 확인하였다.

Keywords

References

  1. Sauer, F. and Kabuka, M., 'Multimedia technology in the radiology department,' Proceedings of the second ACM international conference on Multimedia, pp.263-269, 1994
  2. Sonka, M., Hlavac, V., and Boyle, R., Image Processing Analysis and Machine Vision, PWS Publishing, 1999
  3. Chu, W. W., Cardenas, A. F., and Taira, R. K., 'KMeD: A knowledge-based multimedia medical distributed database system,' Information Systems, Vol.20, No.2, pp.75-96, 1995 https://doi.org/10.1016/0306-4379(95)98556-S
  4. Rui, Y., Huang, T. S., and Chang, S. F., 'Image Retrieval: Current Techniques, Promising Directions and Open Issues,' Journal of Visual Communication and Image Representation, Vol.10, No.4, pp.39-62, 1999 https://doi.org/10.1006/jvci.1999.0413
  5. Flickner, M. et al, 'Query by image content: The QBIC System,' IEEE Computer Special issue on Content Based Retrieval, Vol.28, No.9, pp.23-32, 1995
  6. Chang, N. S. and Fu, K. S., 'Query-by pictorial-example,' IEEE Transactions on Software Engineering, Vol.6, No.6, pp.519-524, 1980
  7. Chang, S. K. and Hsu, A., 'Image information systems: Where do we go from here?,' IEEE Transactions on Knowledge and Data Engineering, Vol.4, No.5, pp.431-442, 1992 https://doi.org/10.1109/69.166986
  8. Gudivada, V. N. and Raghavan, J. V., 'Special issue on content-based image retrieval systems,' IEEE Computer Magazine, Vol.28, No.9, pp.18-62, 1995
  9. Niblack, W., Barber, R., Equitz, W., Flickner, M., Glasman, E., Petkovic, D., Yanker, P., Faloutsos, C., and Taubin, G., 'Efficient and effective querying by image content,' Journal of Intelligent Information Systems, Vol.3, No.3, pp.231-262, 1994 https://doi.org/10.1007/BF00962238
  10. Quinlan, J. R., C4.5: Programs for machine learning, Morgan Kaufmann, 1993
  11. Witten, I. H. and Frank, E., Data Mining: Practical Machine Learning Tools and Techniques with JAVA Implementations, Morgan Kaufmann, 2000
  12. Mitchell, T. M., Machine learning, McGraw-Hill, New York, 1997
  13. 송곡아생, 뇌종양, 대학서림, 1991
  14. 대한신경외과학회, 신경 외과학, 대한신경외과학회, 2000
  15. The Whole Brain Atlas, http://www.med.harvard.edu/AANLIB
  16. Ogle, V. E. and Stonebraker, M., 'Chabot : Retrieval from a relational database of images,' Proceedings of International Conference on IEEE Computer, Vol.28, No.9, pp.40-48, 1995 https://doi.org/10.1109/2.410150
  17. Chu, W. W., Hsu, C. C., Ieong, I. T., and Taira, R. K., 'Content-based Image Retrieval Using Metadata and Relaxation Techniques,' Managing Multimedia Data, McGraw-Hill, 1998
  18. Shyu, C. R., Brodley, C. E., Kak, A. C., Kosaka, A., Aisen, A., and Broderick, L., 'Assert: A physician in-the-loop content-based retrieval system for HRCT image databases,' Computer Vision and Image Understanding, Vol.75, No.12, pp.111-132, 1999 https://doi.org/10.1006/cviu.1999.0768