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Image-Based Informatics for Preclinical Biomedical Research

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4292))

Abstract

In 2006, the New England Journal of Medicine selected medical imaging as one of the eleven most important innovations of the past 1,000 years, primarily due to its ability to allow physicians and researchers to visualize the very nature of disease. As a result of the broad-based adoption of micro imaging technologies, preclinical researchers today are generating terabytes of image data from both anatomic and functional imaging modes. In this paper we describe our early research to apply content-based image retrieval to index and manage large image libraries generated in the study of amyloid disease in mice. Amyloidosis is associated with diseases such as Alzheimer’s, type 2 diabetes, chronic inflammation and myeloma. In particular, we will focus on results to date in the area of small animal organ segmentation and description for CT, SPECT, and PET modes and present a small set of preliminary retrieval results for a specific disease state in kidney CT cross-sections.

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References

  1. Santini, S.: Exploratory Image Databases, Content-based Retrieval. Academic Press, San Fransisco (2001)

    Google Scholar 

  2. Shyu, C., et al.: ASSERT, A physician-in-the-loop content-based image retrieval system for HRCT image databases. Computer Vision and Image Understanding 75(1/2), 111–132 (1999)

    Article  Google Scholar 

  3. Tobin, K.W., et al.: Content-based Image Retrieval for Semiconductor Process Characterization. Journal on Applied Signal Processing 2002(7) (2002)

    Google Scholar 

  4. Tobin, K.W., Karnowski, T.P., Arrowood, L.F., Lakhani, F.: Field Test Results of an Automated Image Retrieval System. In: 12th Annual IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop, Munich, Germany (2001)

    Google Scholar 

  5. Tobin, K.W., Bhaduri, B.L., Bright, E.A., Cheriyadat, A., Karnowski, T.P., Palathingal, P.J., Potok, T.E., Price, J.R.: Automated Feature Generation in Large-Scale Geospatial Libraries for Content-Based Indexing. Journal of Photogrammetric Engineering and Remote Sensing 72(5) (2006)

    Google Scholar 

  6. Merlini, G., Bellotti, V.: Molecular mechanisms of amyloidosis. N. Engl. J. Med. 349(6), 583–596 (2003)

    Article  Google Scholar 

  7. Bellotti, V., Mangione, P., Merlini, G.: Review: immunoglobulin light chain amyloidosis–the archetype of structural and pathogenic variability. J. Struct. Biol. 130(2-3), 280–289 (2000)

    Article  Google Scholar 

  8. Wall, J.S., et al.: Radioimaging of Primary (AL) Amyloidosis with an Amyloid-Reactive Monoclonal Antibody. In: Amyloid and Amyloidosis: Proceedings of the Xth International Symposium on Amyloidosis. CRC Press, Tours (2005)

    Google Scholar 

  9. Schell, M., et al.: Prevention of AA-amyloidosis by active immunotherapy. In: Amyloid and Amyloidosis: Proceedings of the IXth International Symposium on Amyloidosis. David Apathy, Budapest (2001)

    Google Scholar 

  10. Wall, J.S., et al.: Quantitative high-resolution microradiographic imaging of amyloid deposits in a novel murine model of AA amyloidosis. Amyloid 12(3), 149–156 (2005)

    Article  Google Scholar 

  11. Gregor, J., Benson, T., Gleason, S., Paulus, P.: Support algorithms for x-ray micro-CT conebeam imaging. In: Int. Conf. Fully 3D Image Reconstruction in Radiology and Nuclear Medicine, Saint Malo, France (2003)

    Google Scholar 

  12. Gregor, J., Gleason, S., Paulus, M., Cates, J.: Fast Feldkamp reconstruction based on focus of attention and distributed computing. Int. J. Imaging Systems and Technology 12, 229–234 (2002)

    Article  Google Scholar 

  13. Benson, T., Gregor, J.: Distributed iterative image reconstruction for micro-CT with ordered-subsets and focus of attention problem reduction. J. X-Ray Systems and Technology 12, 231–240 (2004)

    Google Scholar 

  14. Benson, T., Gregor, J.: Modified simultaneous iterative reconstruction technique for faster parallel computation. In: IEEE Medical Imaging Conf. 2005, Puerto Rico (2005)

    Google Scholar 

  15. Gregor, J., Gleason, S., Kennel, S., Paulus, M., Benson, T., Wall, J.: Approximate volumetric system models for microSPECT. In: IEEE Medical Imaging Conf. 2004, Rome, Italy (2004)

    Google Scholar 

  16. Yap, J.T., et al.: Combined Clinical PET/CT and micro PET Small Animal Imaging. In: IEEE Nuclear Symposium, vol. 5, pp. 1082–3654, 2995–2998 (2004)

    Google Scholar 

  17. Gleason S, S.-S.H., Abidi, M., Karakashian, F., Morandi, F.: A New Deformable Model for Analysis of X-ray CT Images in Preclinical Studies of Mice for Polycystic Kidney Disease. IEEE Trans. on Medical Imaging, 21 (2002)

    Google Scholar 

  18. Aykac, D., Price, J.R., Wall, J.: 3D Segmentation of the Mouse Spleen in microCT via Active Contours. In: Proceedings of the IEEE, Nuclear Science Symposium and Medical Imaging Conference, Puerto Rico (2005)

    Google Scholar 

  19. Wall, J.S., Kennel, S.J., Paulus, M.J., Gleason, S.S., Gregor, J., Baba, J., Schell, M., Richey, T., O’Nuallain, B., Donnell, R., Hawkins, P.N., Weiss, D.T., Solomon, A.: Quantitative high-resolution microradiographic imaging of amyloid deposits in a novel murine model of AA amyloidosis. Amyloid 12(3), 149–156 (2005)

    Article  Google Scholar 

  20. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  21. Price, J., Aykac, D., Wall, J.: A 3D level sets method for segmenting the mouse spleen and follicles in volumetric microCT images. In: IEEE Engineering in Medicine and Biology Conference (EMBC) (2006)

    Google Scholar 

  22. Besl, P., Mckay, N.D.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)

    Article  Google Scholar 

  23. Micheals, R., Boult, T.E.: Increasing robustness in self-localization and pose estimation. In: Proceedings of the 1999 Mobile Robots XIV, SPIE (1999)

    Google Scholar 

  24. Muller, H., et al.: A Review of Content-Based Image Retrieval Systems in Medical Applications - Clinical Benets and Future Directions. International Journal of Medical Informatics 73(1), 1–23 (2004)

    Article  Google Scholar 

  25. Chu, W.W., Cardenas, A.F., Taira, R.K.: Knowledge-based image retrieval with spatial and temporal constructs. IEEE Trans. on Knowledge and Data Engineering 10(6), 872–888 (1998)

    Article  Google Scholar 

  26. Bueno, J.M., et al.: How to Add Content-based Image Retrieval Capability in a PACS. In: The 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002) (2002)

    Google Scholar 

  27. Schultz, C.P., et al.: Molecular Imaging Portal: New Development IT Platform for Imaging, Nonimaging and Genomics. Molecular Imaging 4(4), 71–77 (2005)

    Google Scholar 

  28. Le Bozec, C., Zapletal, E., Jaulent, M., Heudes, D., Degoulet, P.: Towards content-based image retrieval in a HIS-integrated PACS. In: Proceedings AIMA Symposium (2000)

    Google Scholar 

  29. Smeulders, A.W.M., et al.: Content-Based Image Retrieval at the End of the Early Years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)

    Article  Google Scholar 

  30. Arya, S., et al.: An Optimal Algorithm for Approximate Nearest Neighbor Searching in Fixed Dimensions. In: Proc. of the Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 573–582 (1994)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Tobin, K.W. et al. (2006). Image-Based Informatics for Preclinical Biomedical Research. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919629_82

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  • DOI: https://doi.org/10.1007/11919629_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48626-8

  • Online ISBN: 978-3-540-48627-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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