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A Hybrid Tool for Data Mining in Picture Archiving System

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2123))

Abstract

A tool and a methodology for data mining in picture archiving systems are presented. It is intended to discover the relevant knowledge for picture analysis and diagnosis from the database of image descriptions. Knowledge engineering methods are used to obtain a list of attributes for symbolic image descriptions. An expert describes images according to this list, and stores descriptions in the database. Digital image processing can be applied to improve imaging of specific image features or to get expert-independent feature evaluation. Decision tree induction is used to learn the expert knowledge, presented in the form of image descriptions in the database. Constructed decision tree presents effective models of decision-making, which can be learned to support image classification by the expert. A tool for data mining and image processing is presented. The developed tool and methodology have been tested in the task of early differential diagnosis of pulmonary nodules in lung tomograms and was effective for preclinical diagnosis of peripheral lung cancer, so that we applied the developed methodology of data mining in other medical tasks such as lymph node diagnosis in MRI and investigation of breast MRI.

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References

  1. In: Proceedings of 15th Symposium for Computer Applications in Radiology: Filmless radiology-reengineering the practice of Radiology for the 21st Century. Baltimore, USA 1998. J. of Digital Imaging, Vol. 11,3, Suppl. 1 (1998).

    Google Scholar 

  2. Andriole, K.P.: Anatomy of Picture archiving and communication systems: Nuts and Bolts-Image Acquisition: getting digital images from imaging modalities. J. of Digital Imaging, Vol. 12,2, Suppl. 1 (2000) 216–217.

    Article  Google Scholar 

  3. In: Proceedings of 16th Symposium for Computer Applications in Radiology. PACS: Performance Improvement in Radiology. Houston, USA 1999. J. of Digital Imaging, Vol. 12,2, Suppl. 1 (2000).

    Google Scholar 

  4. In:Proceedingsof 17thSymposium for Computer Applicationsin Radiology:The electronic Practice: Radiology and Enterprise. Philadelphia, USA 2000. J. of Digital Imaging, Vol 13,2, Suppl. 1 (2000).

    Google Scholar 

  5. In: Proceedings of SPIE International Symposium Medical Imaging 1998, San-Diego, USA. SPIE, Vol. 3338 (1998).

    Google Scholar 

  6. Proceedings of SPIE International Symposium Medical Imaging 2000”, San-Diego, USA. SPIE, Vol. 3981. (2000).

    Google Scholar 

  7. In: Proceedings of 14th Int. Congress on Assisted Radiology and Surgery-CARS’2000, San-Francisco, USA. Int. Congress Series, Vol. 1214. Excerpta Medica (2000).

    Google Scholar 

  8. Heywang-Köbrunner, S., Perner, P.: Optimized Computer-Assistet Diagnosis based on Data Mining, Expert Knowledge and Histological Verification. IBaI Report ISSN 1431-2360 (1998).

    Google Scholar 

  9. Perner, P.A.: Knowledge-based image inspection system for automatic defect recognition, classification, and process diagnosis. Int. J. on Machine Vision and Applications 7 (1994) 135–147.

    Article  Google Scholar 

  10. Boose, J. H., Shema, D. B., Bradshaw, J.M.: Recent progress in Aquinas: a knowledge acquisition workbench. Knowledge Acquisition 1 (1989) 185–214.

    Article  Google Scholar 

  11. Kehoe, A. and Parker, G.A.: An KB defect classification system for automated industrial radiographie inspection. IEEE Expert Systems 8 (1991) 149–157.

    Article  Google Scholar 

  12. Schröder, S., Niemann, H., Sagerer, G.: Knowledge acquisition for a knowledge based image analysis system. In: Proc. of the European Knowledge-Acquisition Workshop (EKAW 88). Bosse, J., Gaines, B. (eds.), GMD-Studien, Vol. 143, Sankt Augustin (1988).

    Google Scholar 

  13. Kolodner, J.L., Simpson, R. L., Sycara, K.: A Process Model of Case-Based Reasoning in Problem Solving. In: Proc. 9th Int. Joint conf. on Artificial Intelligence. Los Angeles, CA, (1985) 100–110.

    Google Scholar 

  14. Perner, P.: Case-Based Reasoning for the Low-level and High-level Unit of an Image Interpretation System. In: Advances in Patter Recognition. Singh S. (ed.). Springer-Verlag (1998) 45–54.

    Google Scholar 

  15. Megalooikonomou, K., Davatzikos, C, Herskovits, E.: Mining lesion-defect associations in a brain image database, in Proc. Int. Conf. Knowledge Discovery and Data Mining (KDD’99), San Diego, California, August 1999, 347–351, 1999.

    Google Scholar 

  16. Eklund, P. W., You, J., Deer, P.: Mining remote sensing image data: an integration of fuzzy set theory and image understanding techniques for environmental change detection. In: Data Mining and Knowledge Discovery:Theory, Tools, and Technology. Belur V. Dasarathy (eds.). SPIE, Vol. 4057 (2000) 265–273.

    Google Scholar 

  17. Burl, M. C, Lucchetti, D.: Autonomous visual discovery. In: Data Mining and Knowledge Discovery: Theory, Tools, and Technology, Belur V. Dasarathy (eds.). SPIE, Vol. 4057 (2000) 240–250.

    Google Scholar 

  18. Zaiane, O. R., Han, J.: Discovery spatial associations in Image. In: Data Mining and Knowledge Discovery: Theory, Tools, and Technology. Belur V. Dasarathy (eds.), SPIE, Vol. 4057 (2000) 138–148.

    Google Scholar 

  19. Zamperoni, P.: Feature Extraction, In:, Progress in Picture Processing, Eds. H. Maitre and J. Zinn-Justin. Elsevier Science (1996) 121–184.

    Google Scholar 

  20. Weiss, S.: Predictive Data Mining, Kluwer Verlag (1996).

    Google Scholar 

  21. Perner, P.: Data Mininig on Multimedia Data, Springer Verlag (2001) (to appear).

    Google Scholar 

  22. Belikova, T. P., Yashunskaya, N. I,. Koganm, E.A. Computer-Aided differential Diagnosis of Small solitary Pulmonary Nodules. Computer and Biomedical Research, Vol. 29, 1 (1996) 48–62.

    Article  Google Scholar 

  23. Belikova, T.P., Yashunskaya, N. I,. Kogan, E. A.: Computer analysis for differential diagnosis of small pulmonary nodules. In: Proc. of Int. Congress for lung cancer. Athens Greece, Monduzzi. Editore. Intern. (1994) 93–98.

    Google Scholar 

  24. Perner, P., Belikova, T.P., Yashunskaya, N. I. Knowledge Acquisition by symbolic decision tree induction for interpretation of digital images in radiology. In: Advances in Structural and Syntactical Pattern Recognition. Lecture Notes in Computer Science. Perner, P, Wang, P., Rosenfeld, (eds). Springer, Vol. 1121 (1996). 208–219.

    Google Scholar 

  25. Data Mining Tool Decision Master. http://ww.ibai_solution.de.

  26. Baird, H. S., Mallows C.L.: Bounded-Error in Pre-classification Trees. In: Shape, Structure and Pattern Recognition. Dori, D., Bruckstein, A (eds.) World Scientific Publishing Co, (1995) 100–110.

    Google Scholar 

  27. Quinlain, J.R.: Simplifying decision tree. Intern. Journal on Man-Machine Studies, Vol. 27, (1987) 221–234.

    Article  Google Scholar 

  28. Perner, P.: How to use Repertory Grid for Knowledge Acquisition in Image Interpretation. HTWK Report 2 (1994).

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences IBaI, Arno-Nitzsche-Str. 45, 04277, Leipzig, Germany

    Petra Perner

  2. Institute for Information Transmission Problems Russian Academy of Sciences, B.Karetniy 19, 101447, Moscow, Russia

    Tatjana Belikova

Authors
  1. Petra Perner
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  2. Tatjana Belikova
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arno-Nitzsche-Str. 45, 04277, Leipzig, Germany

    Petra Perner

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© 2001 Springer-Verlag Berlin Heidelberg

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Perner, P., Belikova, T. (2001). A Hybrid Tool for Data Mining in Picture Archiving System. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2001. Lecture Notes in Computer Science(), vol 2123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44596-X_12

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  • DOI: https://doi.org/10.1007/3-540-44596-X_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42359-1

  • Online ISBN: 978-3-540-44596-8

  • eBook Packages: Springer Book Archive

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