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Biomedical Imaging Informatics

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Biomedical Informatics

Abstract

After reading this chapter, you should know the answers to these questions:

  • What makes images a challenging type of data to be processed by computers when compared to non-image clinical data?

  • Why are there many different imaging modalities, and by what major two characteristics do they differ?

  • How are visual and knowledge content in images represented computationally? How are these techniques similar to representation of non-image biomedical data?

  • What sort of applications can be developed to make use of the semantic image content made accessible using the Annotation and Image Markup model?

  • What are four different types of image processing methods? Why are such methods assembled into a pipeline when creating imaging applications?

  • What is an imaging modality with high spatial resolution? What is a modality that provides functional information? Why are most imaging modalities not capable of providing both?

  • What is the goal in performing segmentation in image analysis? Why is there more than one segmentation method?

  • What are two types of quantitative information in images? What are two types of semantic information in images? How might this information be used in medical applications?

  • What is the difference between image registration and image fusion? What are examples of each?

This chapter is adapted from an earlier version in the third edition authored by James F. Brinkley and Robert A. Greenes.

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Notes

  1. 1.

    Frederick Barnard, “One look is worth a thousand words,” Printers’ Ink, December, 1921.

  2. 2.

    http://ncmir.ucsd.edu/ (accessed 4/26/13).

References

  • Agrawal, M., Harwood, D., et al. (2000). Three-dimensional ultrastructure from transmission electron micropscope tilt series. In Proceedings, Second Indian Conference on Vision, Graphics and Image Processing, Bangaore.

    Google Scholar 

  • Aine, C. J. (1995). A conceptual overview and critique of functional neuroimaging techniques in humans I. MRI/fMRI and PET. Critical Reviews in Neurobiology, 9, 229–309.

    PubMed  CAS  Google Scholar 

  • Alberini, J. L., Edeline, V., et al. (2011). Single photon emission tomography/computed tomography (SPET/CT) and positron emission tomography/computed tomography (PET/CT) to image cancer. Journal of Surgical Oncology, 103(6), 602–606.

    PubMed  Google Scholar 

  • André, B., Vercauteren, T., et al. (2009). Introducing space and time in local feature-based endomicroscopic image retrieval. Medical content-based retrieval for clinical decision support. In B. Caputo, H. Mller, T. Syeda-Mahmood, et al. (Eds.), Lecture notes in computer science (Vol. 5853, pp. 18–30). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Appel, B. (2001). Nomenclature and classification of lumbar disc pathology. Neuroradiology, 43(12), 1124–1125.

    PubMed  CAS  Google Scholar 

  • Armstrong, R. A. (2010). Review paper. Quantitative methods in neuropathology. Folia Neuropathologica, 48(4), 217–230.

    PubMed  Google Scholar 

  • Ashburner, J., & Friston, K. J. (1997). Multimodal image coregistration and partitioning – a unified framework. NeuroImage, 6(3), 209–217.

    PubMed  CAS  Google Scholar 

  • Avni. (2009). Addressing the ImageClef 2009 Challenge Using a Patch-based Visual Words Representation %U http://www.clef-campaign.org/2009/working_notes/avni-paperCLEF2009.pdf. Working Notes CLEF2009.

  • Baader, F. E., McGuinness, D. E., et al. (Eds.). (2003). The description logic handbook: Theory, implementation and applications. New York: Cambridge University Press.

    Google Scholar 

  • Baker, J. A., Kornguth, P. J., et al. (1995). Breast cancer: Prediction with artificial neural network based on BI-RADS standardized lexicon. Radiology, 196(3), 817–822.

    PubMed  CAS  Google Scholar 

  • Baumann, B., Gotzinger, E., et al. (2010). Segmentation and quantification of retinal lesions in age-related macular degeneration using polarization-sensitive optical coherence tomography. Journal of Biomedical Optics, 15(6), 061704.

    PubMed  Google Scholar 

  • Bechhofer, S., van Harmelen, F., et al. (2004). OWL Web Ontology Language reference (Technical Report REC-owl-ref-20040210). The WorldWideWeb Consortium. Available from http://www.w3.org/TR/2004/REC-owl-ref-20040210/

  • Becich, M. J. (2000). The role of the pathologist as tissue refiner and data miner: The impact of functional genomics on the modern pathology laboratory and the critical roles of pathology informatics and bioinformatics. Molecular Diagnosis, 5(4), 287–299.

    PubMed  CAS  Google Scholar 

  • Bennett, T. J., & Barry, C. J. (2009). Ophthalmic imaging today: An ophthalmic photographer’s viewpoint – a review. Clinical and Experimental Ophthalmology, 37(1), 2–13.

    PubMed  Google Scholar 

  • Bidgood, W. D., Jr., & Horii, S. C. (1992). Introduction to the ACR-NEMA DICOM standard. Radiographics, 12(2), 345–355.

    PubMed  Google Scholar 

  • Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford: Clarendon Press.

    Google Scholar 

  • Biswal, S., Resnick, D. L., et al. (2007). Molecular imaging: integration of molecular imaging into the musculoskeletal imaging practice. Radiology, 244(3), 651–671.

    PubMed  Google Scholar 

  • Bittorf, A., & Bauer, J., et al. (1997). Web-based training modules in dermatology. MD Comput, 14(5): 371–376, 381.

    Google Scholar 

  • Bloom, F. E., & Young, W. G. (1993). Brain browser. New York: Academic Press.

    Google Scholar 

  • Bodenreider, O. (2008). Biomedical ontologies in action: role in knowledge management, data integration and decision support. Yearbook of Medical Informatics, 67–79.

    Google Scholar 

  • Bosch, A., Munoz, X., et al. (2006). Modeling and classifying breast tissue density in mammograms. Computer Vision and Pattern Recognition, IEEE Computer Society Conference, 2, 1552–1558.

    Google Scholar 

  • Bowden, D. M., & Martin, R. F. (1995). Neuronames brain hierarchy. NeuroImage, 2, 63–83.

    PubMed  CAS  Google Scholar 

  • Brain Innovation, B.V. (2001). BrainVoyager. From http://www.BrainVoyager.de/

  • Brinkley, J. F. (1985). Knowledge-driven ultrasonic three-dimensional organ modelling. Patiernanalysis and Machine Intelligence, PAMI-7(4), 431–441.

    Google Scholar 

  • Brinkley, J. F. (1992). Hierarchical geometric constraint networks as a representation for spatial structural knowledge. Proceedings of the 16th Annual Symposium on Computer Applications in Medical Care, 140–144.

    Google Scholar 

  • Brinkley, J. F. (1993a). A flexible, generic model for anatomic shape: Application to interactive two-dimensional medical image segmentation and matching. Computers and Biomedical Research, 26, 121–142.

    PubMed  CAS  Google Scholar 

  • Brinkley, J. F. (1993b). The potential for three-dimensional ultrasound. In F. A. Chervenak, G. C. Isaacson, & S. Campbell (Eds.), Ultrasound in obstetrics and gynecology. Boston: Little, Brown and Company.

    Google Scholar 

  • Brinkley, J. F., Bradley, S. W., et al. (1997). The digital anatomist information system and its use in the generation and delivery of Web-based anatomy atlases. Computers and Biomedical Research, 30, 472–503.

    PubMed  CAS  Google Scholar 

  • Brinkley, J. F., Wong, B. A., et al. (1999). Design of an anatomy information system. Computer Graphics and Applications, 19(3), 38–48.

    Google Scholar 

  • Brown, D.B., Gould, J.E., et al. (2009). Transcatheter Therapy for Hepatic Malignancy: Standardization of Terminology and Reporting Criteria. Journal of Vascular and Interventional Radiology 20(7): S425–S434. (Reprinted from Journal of Vascular and Interventional Radiology, 18, 1469–1478, 2007)

    Google Scholar 

  • Bug, W. J., Ascoli, G. A., et al. (2008). The NIFSTD and BIRNLex vocabularies: Building comprehensive ontologies for neuroscience. Neuroinformatics, 6(3), 175–194.

    PubMed  Google Scholar 

  • Burnside, E., Rubin, D., et al. (2000). A Bayesian network for mammography. Proceedings of the AMIA Symposium, 106–110.

    Google Scholar 

  • Burnside, E. S., Rubin, D. L., et al. (2004a). Using a Bayesian network to predict the probability and type of breast cancer represented by microcalcifications on mammography. Studies in Health Technology and Informatics, 107(Pt 1), 13–17.

    PubMed  Google Scholar 

  • Burnside, E. S., Rubin, D. L., et al. (2004b). A probabilistic expert system that provides automated mammographic-histologic correlation: Initial experience. AJR. American Journal of Roentgenology, 182(2), 481–488.

    PubMed  Google Scholar 

  • Burnside, E. S., Rubin, D. L., Fine, J. P., et al. (2006). Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: Initial experience. Radiology, 240(3), 666–673.

    PubMed  Google Scholar 

  • Burnside, E. S., Ochsner, J. E., et al. (2007). Use of microcalcification descriptors in BI-RADS 4th edition to stratify risk of malignancy. Radiology, 242(2), 388–395.

    PubMed  Google Scholar 

  • Burnside, E. S., Davis, J., et al. (2009). Probabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings. Radiology, 251(3), 663–672.

    PubMed  Google Scholar 

  • Buxton, R. B. (2009). Introduction to functional magnetic resonance imaging: Principles and techniques. Cambridge/New York: Cambridge University Press.

    Google Scholar 

  • Cabrera Fernandez, D., Salinas, H. M., et al. (2005). Automated detection of retinal layer structures on optical coherence tomography images. Optics Express, 13(25), 10200–10216.

    PubMed  Google Scholar 

  • Caputo, B., Tornmasi, T., et al. (2008). Discriminative cue integration for medical image annotation. Pattern Recognition Letters, 29(15), 1996–2002.

    Google Scholar 

  • Carpenter, A. E., Jones, T. R., et al. (2006). CellProfiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biology, 7(10), R100.

    PubMed  Google Scholar 

  • Caviness, V. S., Meyer, J., et al. (1996). MRI-based topographic parcellation of human neocortex: An anatomically specified method with estimate of reliability. Journal of Cognitive Neuroscience, 8(6), 566–587.

    PubMed  Google Scholar 

  • Chan, E. Y., Qian, W. J., Diamond, D. L., et al. (2007). Quantitative analysis of human immunodeficiency virus type 1-infected CD4+ cell proteome: Dysregulated cell cycle progression and nuclear transport coincide with robust virus production. Journal of Virology, 81, 7571–7583.

    PubMed  CAS  Google Scholar 

  • Channin, D. S., Mongkolwat, P., et al. (2009a). Computing human image annotation. Conference of the Proceeding IEEE Engineering in Medicine and Biology Society, 1, 7065–7068.

    Google Scholar 

  • Channin, D. S., Mongkolwat, P., et al. (2009b). The caBIG annotation and image markup project. Journal of Digital Imaging, 23(2), 217–225.

    PubMed  Google Scholar 

  • Choi, H. S., Haynor, D. R., et al. (1991). Partial volume tissue classification of multichannel magnetic resonance images – a mixel model. IEEE Transactions on Medical Imaging, 10(3), 395–407.

    PubMed  CAS  Google Scholar 

  • Cimino, J. J. (1996). Review paper: Coding systems in health care. Methods of Information in Medicine, 35(4–5), 273–284.

    PubMed  CAS  Google Scholar 

  • Clarysse, P., Friboulet, D., et al. (1997). Tracking geometrical descriptors on 3-D deformable surfaces: Application to the left-ventricular surface of the heart. IEEE Transactions on Medical Imaging, 16(4), 392–404.

    PubMed  CAS  Google Scholar 

  • Cohen, J.D. (2001). FisWidgets. From http://neurocog.lrdc.pitt.edu/fiswidgets/

  • Collins, D. L., Neelin, P., et al. (1994). Automatic 3-D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography, 18(2), 192–205.

    PubMed  CAS  Google Scholar 

  • Collins, D. L., Holmes, D. J., et al. (1995). Automatic 3-D model-based neuroanatomical segmentation. Human Brain Mapping, 3, 190–208.

    Google Scholar 

  • Comaniciu, D., & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619.

    Google Scholar 

  • Corina, D.P., Poliakov, A.V., et al. (2000). Correspondences between language cortex identified by cortical stimulation mapping and fMRI. Neuroimage (Human Brain Mapping Annual Meeting, June 12–16), 11(5), S295.

    Google Scholar 

  • Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29, 162–173.

    PubMed  CAS  Google Scholar 

  • D’Orsi, C. J., & Newell, M. S. (2007). BI-RADS decoded: Detailed guidance on potentially confusing issues. Radiologic Clinics of North America, 45(5), 751–763. v.

    PubMed  Google Scholar 

  • Dale, A. M., Fischl, B., et al. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage, 9(2), 179–194.

    PubMed  CAS  Google Scholar 

  • Dameron, O., Roques E., et al. (2006). Grading lung tumors using OWL-DL based reasoning. 9th International Protégé Conference. Stanford.

    Google Scholar 

  • Datta, R., Joshi, D., et al. (2008). Image retrieval: ideas, influences, and trends of the new age. Acm Computing Surveys, 40(2).

    Google Scholar 

  • Davatzikos, C., & Bryan, R. N. (1996). Using a deformable surface model to obtain a shape representation of the cortex. IEEE Transactions on Medical Imaging, 15(6), 785–795.

    PubMed  CAS  Google Scholar 

  • de Figueiredo, E. H., Borgonovi, A. F., et al. (2011). Basic concepts of MR imaging, diffusion MR imaging, and diffusion tensor imaging. Magnetic Resonance Imaging Clinics of North America, 19(1), 1–22.

    PubMed  Google Scholar 

  • Dempster, A. P., Laird, N. M., et al. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B, 39, 1–38.

    Google Scholar 

  • Deselaers, T., Hegerath, A., et al. (2006). Sparse patch-histograms for object classification in cluttered images. In DAGM 2006, Pattern Recognition, 27th DAGM Symposium, Lecture Notes in Computer Science (pp. 202–211).

    Google Scholar 

  • Deselaers, T., Muller, H., et al. (2007). The CLEF 2005 automatic medical image annotation task. International Journal of Computer Vision, 74(1), 51–58.

    Google Scholar 

  • Deserno, T. M., Antani, S., et al. (2009). Ontology of gaps in content-based image retrieval. Journal of Digital Imaging, 22(2), 202–215.

    PubMed  Google Scholar 

  • Dhenain, M., Ruffins, S. W., et al. (2001). Three-dimensional digital mouse atlas using high-resolution MRI. Developmental Biology, 232(2), 458–470.

    PubMed  CAS  Google Scholar 

  • Diepgen, T. L., & Eysenbach, G. (1998). Digital images in dermatology and the Dermatology Online Atlas on the World Wide Web. The Journal of Dermatology, 25(12), 782–787.

    PubMed  CAS  Google Scholar 

  • Doi, K. (2007). Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 31(4–5), 198–211.

    PubMed  Google Scholar 

  • Donovan, T., & Manning, D. J. (2007). The radiology task: Bayesian theory and perception. The British Journal of Radiology, 80(954), 389–391.

    PubMed  CAS  Google Scholar 

  • Drury, H. A., & Van Essen, D. C. (1997). Analysis of functional specialization in human cerebral cortex using the visible man surface based atlas. Human Brain Mapping, 5, 233–237.

    Google Scholar 

  • Duda, R. O., Hart, P. E., et al. (2001). Pattern classification. New York: Wiley.

    Google Scholar 

  • Dugas-Phocion, G., Ballester, M. A. G., et al. (2004). “Improved EM-based tissue segmentation and partial volume effect quantification in multi-sequence brain MRI.” Medical Image Computing and Computer-Assisted Intervention – Miccai 2004, Pt 1. Proceedings, 3216, 26–33.

    Google Scholar 

  • Eysenbach, G., Bauer, J., et al. (1998). An international dermatological image atlas on the WWW: Practical use for undergraduate and continuing medical education, patient education and epidemiological research. Studies in Health Technology and Informatics, 52(Pt 2), 788–792.

    PubMed  Google Scholar 

  • Federative Committee on Anatomical Terminology. (1998). Terminologia anatomica. Stuttgart: Thieme.

    Google Scholar 

  • Fei-Fei, L., & Perona, P. (2005). A Bayesian hierarchical model for learning natural scene categories. In Proceedings of IEEE Computer Vision and Pattern Recognition (pp. 524–531), San Diego.

    Google Scholar 

  • Fiala, J. C., & Harris, K. M. (2001). Extending unbiased stereology of brain ultrastructure to three-dimensional volumes. Journal of the American Medical Informatics Association: JAMIA, 8(1), 1–16.

    PubMed  CAS  Google Scholar 

  • Figurska, M., Robaszkiewicz, J., et al. (2010). Optical coherence tomography in imaging of macular diseases. Klinika Oczna, 112(4–6), 138–146.

    PubMed  Google Scholar 

  • Fischl, B., Sereno, M. I., et al. (1999). Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. NeuroImage, 9(2), 195–207.

    PubMed  CAS  Google Scholar 

  • FMRIDB Image Analysis Group. (2001). FSLThe FMRIB Software Libarary. From http://www.fmrib.ox.ac.uk/fsl/index.html

  • Fougerousse, F., Bullen, P., et al. (2000). Human-mouse differences in the embryonic expression of developmental control genes and disease genes. Human Molecular Genetics, 9(2), 165–173.

    PubMed  CAS  Google Scholar 

  • Fox, P. T. (Ed.). (2001). Human brain mapping. New York: Wiley.

    Google Scholar 

  • Frackowiak, R. S. J., Friston, K. J., et al. (Eds.). (1997). Human brain function. New York: Academic Press.

    Google Scholar 

  • Franklin, K. B. J., & Paxinos, G. (1997). The mouse brain in stereotactic coordinates. San Diego: Academic Press.

    Google Scholar 

  • Freton, A., & Finger, P. T. (2012). Spectral domain-optical coherence tomography analysis of choroidal osteoma. The British Journal of Ophthalmology, 96(2), 224–228.

    PubMed  Google Scholar 

  • Friefeld, O., Greenspan, H., et al. (2009). Multiple sclerosis lesion detection using constrained GMM and curve evolution. Journal of Biomedical Imaging, 2009, 1–13.

    Google Scholar 

  • Friston, K. J., Holmes, A. P., et al. (1995). Stastical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 2, 189–210.

    Google Scholar 

  • Gabril, M. Y., & Yousef, G. M. (2010). Informatics for practicing anatomical pathologists: Marking a new era in pathology practice. Modern Pathology, 23(3), 349–358.

    PubMed  Google Scholar 

  • George, J. S., Aine, C. J., et al. (1995). Mapping function in human brain with magnetoencephalography, anatomical magnetic resonance imaging, and functional magnetic resonance imaging. Journal of Clinical Neurophysiology, 12(5), 406–431.

    PubMed  CAS  Google Scholar 

  • Gerstner, E. R., & Sorensen, A. G. (2011). Diffusion and diffusion tensor imaging in brain cancer. Seminars in Radiation Oncology, 21(2), 141–146.

    PubMed  Google Scholar 

  • Giger, M., & MacMahon, H. (1996). Image processing and computer-aided diagnosis. Radiologic Clinics of North America, 34(3), 565–596.

    PubMed  CAS  Google Scholar 

  • Goldberg, S. N., Grassi, C. J., et al. (2009). Image-guided tumor ablation: Standardization of terminology and reporting criteria. Journal of Vascular and Interventional Radiology, 20(7 Suppl), S377–S390.

    PubMed  Google Scholar 

  • Gombas, P., Skepper, J. N., et al. (2004). Past, present and future of digital pathology. Orvosi Hetilap, 145(8), 433–443.

    PubMed  Google Scholar 

  • Gonzalez, R.C., Woods, R.E., et al. (2009). Digital image processing using MATLAB. S.I., Gatesmark Publishing.

    Google Scholar 

  • Grau, B., Horrocks, I., et al. (2008). Chapter 3: Description logics. In B. Porter, V. Lifschitz, & F. Van Harmelen (Eds.), Handbook of knowledge representation (Vol. 28, p. 1005). Amsterdam/Boston: Elsevier.

    Google Scholar 

  • Greenspan, H., & Pinhas, A. T. (2007). Medical image categorization and retrieval for PACS using the GMM-KL framework. IEEE Transactions on Information Technology in Biomedicine, 11(2), 190–202.

    PubMed  Google Scholar 

  • Greenspan, H., Ruf, A., et al. (2006). Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. IEEE Transactions on Medical Imaging, 25(9), 1233–1245.

    PubMed  Google Scholar 

  • Greenspan, H., Avni, U., et al. (2011). X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words. IEEE Transactions on Medical Imaging, 30(3), 733–746.

    PubMed  Google Scholar 

  • Hansell, D. M., Bankier, A. A., et al. (2008). Fleischner society: Glossary of terms for thoracic imaging. Radiology, 246(3), 697–722.

    PubMed  Google Scholar 

  • Hansen, L. K., Nielsen, F. A., et al. (1999). Lyngby – modeler’s Matlab toolbox for spatio-temporal analysis of functional neuroimages. NeuroImage, 9(6), S241.

    Google Scholar 

  • Haralick, R. M. (1988). Mathematical morphology. Seattle: University of Washington.

    Google Scholar 

  • Haralick, R. M., & Shapiro, L. G. (1992). Computer and robot vision. Reading: Addison-Wesley.

    Google Scholar 

  • Harney, A. S., & Meade, T. J. (2010). Molecular imaging of in vivo gene expression. Future Medicinal Chemistry, 2(3), 503–519.

    PubMed  CAS  Google Scholar 

  • Hasan, K. M., Walimuni, I. S., et al. (2010). A review of diffusion tensor magnetic resonance imaging computational methods and software tools. Computers in Biology and Medicine, 41(12), 1062–1072.

    PubMed  Google Scholar 

  • Heiss, W. D., & Phelps, M. E. (Eds.). (1983). Positron emission tomography of the brain. Berlin/New York: Springer.

    Google Scholar 

  • Held, K., Rota Kops, E., et al. (1997). Markov random field segmentation of brain MR images. IEEE Transactions on Medical Imaging, 16(6), 878–886.

    PubMed  CAS  Google Scholar 

  • Henry (Bakken), S. B., & Mead, C. N. (1997). Nursing classification systems: Necessary but not sufficient for representing “what nurses do” for inclusion in computer-based patient record systems. Journal of the American Medical Informatics Association: JAMIA, 4(3), 222–232.

    Google Scholar 

  • Hersh, W., Muller, H., et al. (2009). The ImageCLEFmed medical image retrieval task test collection. Journal of Digital Imaging, 22(6), 648–655.

    PubMed  Google Scholar 

  • Hinshaw, K. P., Poliakov, A. V., et al. (2002). Shape-based cortical surface segmentation for visualization brain mapping. NeuroImage, 16(2), 295–316.

    PubMed  Google Scholar 

  • Hoffman, J. M., & Gambhir, S. S. (2007). Molecular imaging: The vision and opportunity for radiology in the future. Radiology, 244(1), 39–47.

    PubMed  Google Scholar 

  • Hohne, K., Bomans, M., et al. (1990). 3-D visualization of tomographic volume data using the generalized voxel model. The Visual Computer, 6(1), 28–36.

    Google Scholar 

  • Hohne, K.H., Bomans, M., et al. (1992). A volume-based anatomical atlas. IEEE Computer Graphics and Applications, 72–78.

    Google Scholar 

  • Hohne, K. H., Pflesser, B., et al. (1995). A new representation of knowledge concerning human anatomy and function. Nature Medicine, 1(6), 506–510.

    PubMed  CAS  Google Scholar 

  • Hu, Z., Abramoff, M. D., et al. (2010a). Automated segmentation of neural canal opening and optic cup in 3D spectral optical coherence tomography volumes of the optic nerve head. Investigative Ophthalmology and Visual Science, 51(11), 5708–5717.

    PubMed  Google Scholar 

  • Hu, Z., Niemeijer, M., et al. (2010b). Automated segmentation of 3-D spectral OCT retinal blood vessels by neural canal opening false positive suppression. Medical Image Computing and Computer Assisted Intervension, 13(Pt 3), 33–40.

    CAS  Google Scholar 

  • Hudson, D. L., & Cohen, M. E. (2009). Multidimensional medical decision making. Conference Proceedings – IEEE Engineering in Medicine and Biology Society, 1, 3405–3408.

    Google Scholar 

  • International Anatomical Nomenclature Committee. (1989). Nomina anatomica. Edinburgh: Churchill Livingstone.

    Google Scholar 

  • Jiang, Y.-G., Ngo C.-W, et al. (2007). Towards optimal bag-of-features for object categorization and semantic video retrieval. In Proceedings of the 6th ACM international conference on Image and video retrieval (pp. 494–501), Amsterdam: ACM.

    Google Scholar 

  • Johnson, K.A., & Becker, J.A. (2001). The whole brain atlas. From http://www.med.harvard.edu/AANLIB/home.html

  • Jurie, F., & Triggs, B. (2005). Creating efficient codebooks for visual recognition. Proceedings of the tenth IEEE international conference on Computer Vision (ICCV’05) Volume 1 – Volume 01, IEEE Computer Society: 604–610 %@ 600-7695-2334-X-7601.

    Google Scholar 

  • Kahn, C. E., & Rubin, D. L. (2009). Automated semantic indexing of figure captions to improve radiology image retrieval. Journal of the American Medical Informatics Association: JAMIA, 16(3), 380–386.

    PubMed  Google Scholar 

  • Kahn, C. E., Jr., Langlotz, C. P., et al. (2009). Toward best practices in radiology reporting. Radiology, 252(3), 852–856.

    PubMed  Google Scholar 

  • Kang, J. H., & Chung, J. K. (2008). Molecular-genetic imaging based on reporter gene expression. Journal of Nuclear Medicine, 49(Suppl 2), 164S–179S.

    PubMed  CAS  Google Scholar 

  • Kapur, T., Grimson, W. E., et al. (1996). Segmentation of brain tissue from magnetic resonance images. Medical Image Analysis, 1(2), 109–127.

    PubMed  CAS  Google Scholar 

  • Kass, M., Witkin, A., et al. (1987). Snakes: Active contour models. International Journal of Computer Vision, 1(4), 321–331.

    Google Scholar 

  • Kennedy, D. (2001). Internet brain segmentation repository. From http://neuro-www.mgh.harvard.edu/cma/ibsr

  • Kevles, B. (1997). Naked to the bone: Medical imaging in the twentieth century. New Brunswick: Rutgers University Press.

    Google Scholar 

  • Kimborg, D.Y., & Aguirre, G.K. (2002). A flexible architecture for neuroimaging data analysis and presentation. From http://www.nimh.nih.gov/neuroinformatics/kimberg.cfm

  • King, W., Proffitt, J., et al. (2000). The role of fluorescence in situ hybridization technologies in molecular diagnostics and disease management. Molecular Diagnosis, 5(4), 309–319.

    PubMed  CAS  Google Scholar 

  • Korner, M., Weber, C. H., et al. (2007). Advances in digital radiography: Physical principles and system overview. Radiographics, 27(3), 675–686.

    PubMed  Google Scholar 

  • Koslow, S. H., & Huerta, M. F. (Eds.). (1997). Neuroinformatics: An overview of the human brain project. Mahwah: Lawrence Erlbaum.

    Google Scholar 

  • Kremkau, F. W. (2006). Diagnostic ultrasound principles and instruments. St. Louis: Saunders Elsevier.

    Google Scholar 

  • Kulikowski, C. A. (1997). Medical imaging informatics: Challenges of definition and integration. Journal of the American Medical Informatics Association: JAMIA, 4(3), 252–253.

    PubMed  CAS  Google Scholar 

  • Langlotz, C. P. (2006). RadLex: A new method for indexing online educational materials. Radiographics, 26(6), 1595–1597.

    PubMed  Google Scholar 

  • Larabell, C. A., & Nugent, K. A. (2010). Imaging cellular architecture with X-rays. Current Opinion in Structural Biology, 20(5), 623–631.

    PubMed  CAS  Google Scholar 

  • Le Bihan, D., Mangin, J. F., et al. (2001). Diffusion tensor imaging: Concepts and applications. Journal of Magnetic Resonance Imaging, 13(4), 534–546.

    PubMed  Google Scholar 

  • Ledley, R. S., & Lusted, L. B. (1991). Reasoning foundations of medical diagnosis. MD Computing, 8(5), 300–315.

    PubMed  CAS  Google Scholar 

  • Lee, D. H. (2003). Magnetic resonance angiography. Advances in Neurology, 92, 43–52.

    PubMed  Google Scholar 

  • Lee, J. K. T. (2006). Computed body tomography with MRI correlation. Philadelphia: Lippincott Williams & Wilkins.

    Google Scholar 

  • Lee, Y., Kim, N., et al. (2009). Bayesian classifier for predicting malignant renal cysts on MDCT: Early clinical experience. AJR. American Journal of Roentgenology, 193(2), W106–W111.

    PubMed  Google Scholar 

  • Lehmann, T. M., Guld, M. O., et al. (2004). Content-based image retrieval in medical applications. Methods of Information in Medicine, 43(4), 354–361.

    PubMed  CAS  Google Scholar 

  • Leong, F. J., & Leong, A. S. (2003). Digital imaging applications in anatomic pathology. Advances in Anatomic Pathology, 10(2), 88–95.

    PubMed  Google Scholar 

  • Levy, M. A., & Rubin, D. L. (2008). Tool support to enable evaluation of the clinical response to treatment. AMIA Annual Symposium Proceedings, 2008, 399–403.

    Google Scholar 

  • Levy, M. A., & Rubin, D. L. (2011). Current and future trends in imaging informatics for oncology. Cancer Journal, 17(4), 203–210.

    Google Scholar 

  • Levy, M. A., O’Connor, M. J., et al. (2009). Semantic reasoning with image annotations for tumor assessment. AMIA Annual Symposium Proceedings, 2009, 359–363.

    PubMed  Google Scholar 

  • Lexe, G., Monaco, J., et al. (2009). Towards improved cancer diagnosis and prognosis using analysis of gene expression data and computer aided imaging. Experimental Biology and Medicine (Maywood, N.J.), 234(8), 860–879.

    Google Scholar 

  • Lichtenbelt, B., Crane, R., et al. (1998). Introduction to volume rendering. Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Lindberg, D. A. B., Humphreys, B. L., & McCray, A. T. (1993). The unified medical language system. Methods of Information in Medicine, 32, 281–291.

    PubMed  CAS  Google Scholar 

  • Liu, Y. I., Kamaya, A., et al. (2009). A controlled vocabulary to represent sonographic features of the thyroid and its application in a Bayesian network to predict thyroid nodule malignancy. Summit on Translational Bioinformatics, 2009, 68–72.

    PubMed  Google Scholar 

  • Liu, Y. I., Kamaya, A., et al. (2011). A Bayesian network for differentiating benign from malignant thyroid nodules using sonographic and demographic features. AJR. American Journal of Roentgenology, 196(5), W598–W605.

    PubMed  Google Scholar 

  • Lorensen, W. E., & Cline, H. E. (1987). Marching cubes: A high resolution 3-D surface construction algorithm. ACM SIGGRAPH Computer Graphics, 21(4), 163–169.

    Google Scholar 

  • Lowe, D. (1999). Object recognition from local scale invariant features. In Proceedings of the International Conference on Computer Vision (pp. 1150–1157), Greece.

    Google Scholar 

  • Lowe, H.J., Antipov, I., et al. (1998). Towards knowledge-based retrieval of medical images. The role of semantic indexing, image content representation and knowledge-based retrieval. Proceedings of the AMIA Symposium, 882–886.

    Google Scholar 

  • Lusted, L. B. (1960). Logical analysis in roentgen diagnosis. Radiology, 74, 178–193.

    PubMed  CAS  Google Scholar 

  • MacDonald, D. (1993). Register, McConnel Brain Imaging Center. Montreal: Neurological Institute.

    Google Scholar 

  • MacDonald, D., Kabani, N., et al. (2000). Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage, 12(3), 340–356.

    PubMed  CAS  Google Scholar 

  • Margolis, D. J., Hoffman, J. M., et al. (2007). Molecular imaging techniques in body imaging. Radiology, 245(2), 333–356.

    PubMed  Google Scholar 

  • Marquet, G., Dameron, O., et al. (2007). Grading glioma tumors using OWL-DL and NCI Thesaurus. AMIA Annual Symposium Proceedings, 508–512.

    Google Scholar 

  • Marroquin, J. L., Vemuri, B. C., et al. (2002). An accurate and efficient Bayesian method for automatic segmentation of brain MRI. IEEE Transactions on Medical Imaging, 21(8), 934–945.

    PubMed  CAS  Google Scholar 

  • Martin, R. F., & Bowden, D. M. (2001). Primate brain maps: Structure of the macaque brain. New York: Elsevier Science.

    Google Scholar 

  • Martin, R.F., Mejino, J.L.V., et al. (2001). Foundational model of neuroanatomy: implications for the Human Brain Project. Proceedings of the AMIA Annual Fall Symposium, 438–442. Washington, D.C.

    Google Scholar 

  • Marwede, D., Schulz, T., et al. (2008). Indexing thoracic CT reports using a preliminary version of a standardized radiological lexicon (RadLex). Journal of Digital Imaging, 21(4), 363–370.

    PubMed  Google Scholar 

  • Massoud, T. F., & Gambhir, S. S. (2003). Molecular imaging in living subjects: Seeing fundamental biological processes in a new light. Genes & Development, 17, 545–580.

    CAS  Google Scholar 

  • McInerney, T., & Terzopoulos, D. (1997). Medical image segmentation using topologically adaptable surfaces. Cvrmed-Mrcas’97. Lecture Notes in Computer Science, 1205, 23–32.

    Google Scholar 

  • McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York: Wiley.

    Google Scholar 

  • Mechouche, A., Golbreich, C., et al. (2008). Ontology-based annotation of brain MRI images. AMIA Annual Symposium Proceedings, 460–464.

    Google Scholar 

  • Mehta, T. S., Raza, S., et al. (2000). Use of Doppler ultrasound in the evaluation of breast carcinoma. Seminars in Ultrasound, CT, and MR, 21(4), 297–307.

    PubMed  CAS  Google Scholar 

  • Min, J.J., & Gambhir, S.S. (2008). Molecular imaging of PET reporter gene expression. Handbook of Experimental Pharmacology, (185 Pt 2), 277–303.

    Google Scholar 

  • Modayur, B., Prothero, J., et al. (1997). Visualization-based mapping of language function in the brain. NeuroImage, 6, 245–258.

    PubMed  CAS  Google Scholar 

  • Motik, B., Grau, B. C., et al. (2008). OWL 2: The next step for OWL. Journal of Web Semantics, 6(4), 309–322.

    Google Scholar 

  • Motik, B., Shearer, R., et al. (2009). Hypertableau reasoning for description logics. Journal of Artificial Intelligence Research, 36, 165–228.

    Google Scholar 

  • Muller, H., Michoux, N., et al. (2004). A review of content-based image retrieval systems in medical applications-clinical benefits and future directions. International Journal of Medical Informatics, 73(1), 1–23.

    PubMed  Google Scholar 

  • Napel, S. A., Beaulieu, C. F., et al. (2010). Automated retrieval of CT images of liver lesions on the basis of image similarity: Method and preliminary results. Radiology, 256(1), 243–252.

    PubMed  Google Scholar 

  • National Library of Medicine. (1999). Medical subject headingsAnnotated alphabetic list. Bethesda: U.S. Department of Health and Human Services, Public Health Service.

    Google Scholar 

  • Ng, A. Y., M. Jordan, et al. (2001). On spectral clustering: analysis and an algorithm. In Advances in Neural Information Processing Systems (NIPS 13).

    Google Scholar 

  • Nielsen, B., Albregtsen, F., et al. (2008). Statistical nuclear texture analysis in cancer research: A review of methods and applications. Critical Reviews in Oncogenesis, 14(2–3), 89–164.

    PubMed  Google Scholar 

  • Nowak, E., Jurie, F., et al. (2006). "Sampling strategies for bag-of-features image classification." computer vision – Eccv 2006, Pt 4. Proceedings, 3954, 490–503.

    Google Scholar 

  • Organization for Human Brain Mapping. (2001). Annual Conference on Human Brain Mapping. Brighton.

    Google Scholar 

  • Paddock, S. W. (1994). To boldly glow. Applications of laser scanning confocal microscopy in developmental biology. BioEssays, 16(5), 357–365.

    PubMed  CAS  Google Scholar 

  • Paxinos, G., & Watson, C. (1986). The rat brain in stereotaxic coordinates. San Diego: Acedemic Press.

    Google Scholar 

  • Perkins, G., Renken, C., et al. (1997). Electron tomography of neuronal mitochondria: Three-dimensional structure and organization of cristae and menbrane contacts. Journal of Structural Biology, 119(3), 260–272.

    PubMed  CAS  Google Scholar 

  • Pham, D. L., Xu, C. Y., et al. (2000). Current methods in medical image segmentation. Annual Review of Biomedical Engineering, 2, 315.

    PubMed  CAS  Google Scholar 

  • Pouratian, N., Sheth, S. A., et al. (2003). Shedding light on brain mapping: Advances in human optical imaging. Trends in Neurosciences, 26(5), 277–282.

    PubMed  CAS  Google Scholar 

  • Prastawa, M., Gilmore, J., et al. (2004). Automatic segmentation of neonatal brain MRI. Medical Image Computing and Computer-Assisted Intervention – Miccai 2004, Pt 1. Proceedings, 3216, 10–17.

    Google Scholar 

  • Prothero, J. S., & Prothero, J. W. (1986). Three-dimensional reconstruction from serial sections IV. The reassembly problem. Computers and Biomedical Research, 19(4), 3610373.

    Google Scholar 

  • Pysz, M. A., Gambhir, S. S., et al. (2010). Molecular imaging: Current status and emerging strategies. Clinical Radiology, 65(7), 500–516.

    PubMed  CAS  Google Scholar 

  • Qiu, G. (2002). Indexing chromatic and achromatic patterns for content-based colour image retrieval. Pattern Recognition, 35(8), 1675–1686.

    Google Scholar 

  • Rahmani, R., Goldman, S. A., et al. (2008). Localized content-based image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(11), 1902–1912.

    PubMed  Google Scholar 

  • Ray, P. (2011). Multimodality molecular imaging of disease progression in living subjects. Journal of Biosciences, 36(3), 499–504.

    PubMed  Google Scholar 

  • Ray, P., & Gambhir, S. S. (2007). Noninvasive imaging of molecular events with bioluminescent reporter genes in living subjects. Methods in Molecular Biology, 411, 131–144.

    PubMed  CAS  Google Scholar 

  • Rector, A. L., Nowlan, W. A., et al. (1993). Goals for concept representation in the GALEN project. In C. Safran (Ed.), Proceedings of the 17th annual symposium on Computer Applications in Medical Care (SCAMC 93) (pp. 414–418). New York: McGraw Hill.

    Google Scholar 

  • Ribaric, S., Todorovski, L., et al. (2001). Presentation of dermatological images on the internet. Computer Methods and Programs in Biomedicine, 65(2), 111–121.

    PubMed  CAS  Google Scholar 

  • Ritchie, C. J., Edwards, W. S., et al. (1996). Three-dimensional ultrasonic angiography using power-mode Doppler. Ultrasound in Medicine and Biology, 22(3), 277–286.

    PubMed  CAS  Google Scholar 

  • Robinson, P. J. (1997). Radiology’s Achilles’ heel: Error and variation in the interpretation of the Rontgen image. The British Journal of Radiology, 70(839), 1085–1098.

    PubMed  CAS  Google Scholar 

  • Rohlfing, T., & Maurer, C. R., Jr. (2003). Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees. IEEE Transactions on Information Technology in Biomedicine, 7(1), 16–25.

    PubMed  Google Scholar 

  • Rosen, G.D., Williams, A.G., et al. (2000). The mouse brain library @ www.mbl.org. International Mouse Genome Conference, 14, 166.

  • Ross, B., & Bluml, S. (2001). Magnetic resonance spectroscopy of the human brain. Anatomical Record (New Anat), 265(2), 54–84.

    CAS  Google Scholar 

  • Rosse, C. (2000). Terminologia anatomica; considered from the perspective of next-generation knowledge sources. Clinical Anatomy, 14, 120–133.

    Google Scholar 

  • Rosse, C., & Mejino, J. L. V. (2003). A reference ontology for bioinformatics: The foundational model of anatomy. Journal of Bioinformatics, 36(6), 478–500.

    Google Scholar 

  • Rosse, C., Mejino, J. L., et al. (1998a). Motivation and organizational principles for anatomical knowledge representation: The digital anatomist symbolic knowledge base. Journal of the American Medical Informatics Association: JAMIA, 5(1), 17–40.

    PubMed  CAS  Google Scholar 

  • Rosse, C., Shapiro, L.G., et al. (1998b). The Digital Anatomist foundational model: principles for defining and structuring its concept domain. Proceedings, American Medical Informatics Association Fall Symposium (pp. 820–824), Orlando.

    Google Scholar 

  • Rubin, D. L. (2008). Creating and curating a terminology for radiology: Ontology modeling and analysis. Journal of Digital Imaging, 21(4), 355–362.

    PubMed  Google Scholar 

  • Rubin, D. L. (2011, October). Measuring and improving quality in radiology: Meeting the challenge with informatics. Radiographics, 31(6), 1511–1527.

    PubMed  Google Scholar 

  • Rubin, D. L., & Napel, S. (2010). Imaging informatics: toward capturing and processing semantic information in radiology images. Yearbook of Medical Informatics, 34–42.

    Google Scholar 

  • Rubin, D. L., Bashir, Y., et al. (2004). Linking ontologies with three-dimensional models of anatomy to predict the effects of penetrating injuries. Conference Proceedings: IEEE Engineering in Medicine and Biology Society, 5, 3128–3131.

    Google Scholar 

  • Rubin, D. L., Bashir, Y., et al. (2005). Using an ontology of human anatomy to inform reasoning with geometric models. Studies in Health Technology and Informatics, 111, 429–435.

    PubMed  Google Scholar 

  • Rubin, D.L., Grossman, D., et al. (2006a). Ontology-based representation of simulation models of physiology. AMIA Annual Symposium Proceedings, 664–668.

    Google Scholar 

  • Rubin, D. L., Dameron, O., et al. (2006b). Using ontologies linked with geometric models to reason about penetrating injuries. Artificial Intelligence in Medicine, 37(3), 167–176.

    PubMed  Google Scholar 

  • Rubin, D.L., Rodriguez, C., et al. (2008). iPad: Semantic annotation and markup of radiological images. AMIA Annual Symposium Proceedings, 626–630.

    Google Scholar 

  • Rubin, D. L., Talos, I. F., et al. (2009a). Computational neuroanatomy: Ontology-based representation of neural components and connectivity. BMC Bioinformatics, 10(Suppl 2), S3.

    PubMed  Google Scholar 

  • Rubin, D. L., Supekar, K., et al. (2009b). Annotation and image markup: Accessing and interoperating with the semantic content in medical imaging. IEEE Intelligent Systems, 24(1), 57–65.

    Google Scholar 

  • Rubin, D. L., Flanders, A., et al. (2011). Ontology-assisted analysis of Web queries to determine the knowledge radiologists seek. Journal of Digital Imaging, 24(1), 160–164.

    PubMed  Google Scholar 

  • Ruiz, M.E. (2006). Combining image features, case descriptions and UMLS concepts to improve retrieval of medical images. AMIA Annual Symposium Proceedings, 674–678.

    Google Scholar 

  • Sandor, S., & Leahy, R. (1997). Surface-based labeling of cortical anatomy using a deformable atlas. IEEE Transactions on Medical Imaging, 16(1), 41–54.

    PubMed  CAS  Google Scholar 

  • Schaltenbrand, G., & Warren, W. (1977). Atlas for stereotaxy of the human brain. Stuttgart: Thieme.

    Google Scholar 

  • Schimel, A. M., Fisher, Y. L., et al. (2011). Optical coherence tomography in the diagnosis and management of diabetic macular edema: Time-domain versus spectral-domain. Ophthalmic Surgery, Lasers & Imaging, 42(4), S41–S55.

    Google Scholar 

  • Schultz, E. B., Price, C., et al. (1997). Symbolic anatomic knowledge representation in the read codes version 3: Structure and application. Journal of the American Medical Informatics Association: JAMIA, 4, 38–48.

    Google Scholar 

  • Seidenari, S., Pellacani, G., et al. (2003). Computer description of colours in dermoscopic melanocytic lesion images reproducing clinical assessment. British Journal of Dermatology, 149(3), 523–529.

    PubMed  CAS  Google Scholar 

  • Sensor Systems Inc. (2001). MedEx. from http://medx.sensor.com/products/medx/index.html

  • Shapiro, L. G., & Stockman, G. C. (2001). Computer vision. Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Shi, J. B., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888–905.

    Google Scholar 

  • Singh, A., Massoud, T. F., et al. (2008). Molecular imaging of reporter gene expression in prostate cancer: An overview. Seminars in Nuclear Medicine, 38(1), 9–19.

    PubMed  Google Scholar 

  • Sivic, J., & Zisserman, A. (2003). Video Google: A text retrieval approach to object matching in videos. Proceedings of the International Conference on Computer Vision, 2, 1470–1477.

    Google Scholar 

  • Smeulders, A. W. M., Worring, M., et al. (2000). Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1349–1380.

    Google Scholar 

  • Smith, M.K., Welty, C., et al. (2004). OWL web ontology language guide, http://www.w3.org/TR/owl-guide/

  • Smith, M. Q., Staley, C. A., et al. (2009). Multiplexed fluorescence imaging of tumor biomarkers in gene expression and protein levels for personalized and predictive medicine. Current Molecular Medicine, 9(8), 1017–1023.

    PubMed  CAS  Google Scholar 

  • Sohrab, M. A., Smith, R. T., et al. (2011). Imaging characteristics of dry age-related macular degeneration. Seminars in Ophthalmology, 26(3), 156–166.

    PubMed  Google Scholar 

  • Soto, G. E., Young, S. J., et al. (1994). Serial section electron tomography: A method for three-dimensional reconstruction of large structures. NeuroImage, 1, 230–243.

    PubMed  CAS  Google Scholar 

  • Spitzer, V. M., & Whitlock, D. G. (1998). The visible human dataset: The anatomical platform for human simulation. Anatomical Record, 253(2), 49–57.

    PubMed  CAS  Google Scholar 

  • Stensaas, S. S., & Millhouse, O.E. (2001). Atlases of the brain. From http://medstat.med.utah.edu/kw/brain_atlas/credits.htm

  • Subramaniam, B., Hennessey, J. G., et al. (1997). Software and methods for quantitative imaging in neuroscience: The Kennedy Krieger Institute Human Brain Project. In S. H. Koslow & M. F. Huerta (Eds.), Neuroinformatics: An overview of the human brain project (pp. 335–360). Mahwah: Lawrence Erlbaum.

    Google Scholar 

  • Sundsten, J.W., Conley, D.M., et al. (2000). Digital Anatomist web-based interactive atlases. From http://www9.biostr.washington.edu/da.html

  • Swanson, L. W. (1992). Brain maps: Structure of the rat brain. Amsterdam/New York: Elsevier.

    Google Scholar 

  • Swanson, L. W. (1999). Brain maps: Structure of the rat brain. New York: Elsevier Science.

    Google Scholar 

  • Talairach, J., & Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain. New York: Thieme Medical Publishers.

    Google Scholar 

  • Talos, I. F., Rubin, D. L., et al. (2008). A prototype symbolic model of canonical functional neuroanatomy of the motor system. Journal of Biomedical Informatics, 41(2), 251–263.

    PubMed  Google Scholar 

  • Toga, A.W. (2001). UCLA Laboratory for Neuro Imaging (LONI). From http://www.loni.ucla.edu/

  • Toga, A. W., Ambach, K. L., et al. (1994). High-resolution anatomy from in situ human brain. NeuroImage, 1(4), 334–344.

    PubMed  CAS  Google Scholar 

  • Toga, A. W., Santori, E. M., et al. (1995). A 3-D digital map of rat brain. Brain Research Bulletin, 38(1), 77–85.

    PubMed  CAS  Google Scholar 

  • Toga, A. W., Frackowiak, R. S. J., et al. (Eds.). (2001). Neuroimage: A journal of brain function. New York: Academic Press.

    Google Scholar 

  • Tommasi, T., Caputo, B., et al. (2010). Overview of the CLEF 2009 medical image annotation track. In Proceedings of the 10th international conference on cross-language evaluation forum: Multimedia experiments (pp. 85–93). Corfu: Springer.

    Google Scholar 

  • Toomre, D., & Bewersdorf, J. (2010). A new wave of cellular imaging. Annual Review of Cell and Developmental Biology, 26, 285–314.

    PubMed  CAS  Google Scholar 

  • Tsarkov, D., & Horrocks, I. (2006). FaCT++ description logic reasoner: System description. Automated Reasoning, Proceedings, 4130, 292–297.

    Google Scholar 

  • Van Essen, D. C., & Drury, H. A. (1997). Structural and functional analysis of human cerebral cortex using a surface-basec atlas. Journal of Neuroscience, 17(18), 7079–7102.

    PubMed  Google Scholar 

  • Van Essen, D. C., Drury, H. A., et al. (2001). An integrated software suite for surface-based analysis of cerebral cortex. Journal of American Medical Association, 8(5), 443–459.

    Google Scholar 

  • Van Leemput, K., Maes, F., et al. (1999). Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging, 18(10), 897–908.

    PubMed  Google Scholar 

  • Van Noorden, S. (2002). Advances in immunocytochemistry. Folia Histochemica et Cytobiologica, 40(2), 121–124.

    PubMed  Google Scholar 

  • Vapnik, V. N. (2000). The nature of statistical learning theory. New York: Springer.

    Google Scholar 

  • Wang, J. Z., Wiederhold, G., et al. (1997). Content-based image indexing and searching using Daubechies’ wavelets. International Journal on Digital Libraries, 1(4), 311–328.

    Google Scholar 

  • Weissleder, R., & Mahmood, U. (2001). Molecular imaging. Radiology, 219, 316–333.

    PubMed  CAS  Google Scholar 

  • Wellcome Department of Cognitive Neurology. (2001). Statistical parametric mapping. From http://www.fil.ion.ucl.ac.uk/spm/

  • Wessels, J. T., Yamauchi, K., et al. (2010). Advances in cellular, subcellular, and nanoscale imaging in vitro and in vivo. Cytometry. Part A, 77(7), 667–676.

    Google Scholar 

  • Willmann, J. K., van Bruggen, N., et al. (2008). Molecular imaging in drug development. Nature Reviews Drug Discovery, 7(7), 591–607.

    PubMed  CAS  Google Scholar 

  • Wilson, T. (1990). Confocal microscopy. San Diego: Academic Press Ltd.

    Google Scholar 

  • Wong, B.A., Rosse, C., et al. (1999). Semi-automatic scene generation using the Digital Anatomist Foundational Model. Proceedings, American Medical Informatics Association Fall Symposium (pp. 637–641), Washington, D.C.

    Google Scholar 

  • Woods, R. P., Cherry, S. R., et al. (1992). Rapid automated algorithm for aligning and reslicing PET images. Journal of Computer Assisted Tomography, 16, 620–633.

    PubMed  CAS  Google Scholar 

  • Woods, R. P., Mazziotta, J. C., et al. (1993). MRI-PET registration with automated algorithm. Journal of Computer Assisted Tomography, 17, 536–546.

    PubMed  CAS  Google Scholar 

  • WorldWideWeb Consortium. (W3C Recommendation 10 Feb 2004). OWLWeb Ontology Language Reference.

    Google Scholar 

  • Yoo, T. S. (2004). Insight into images: Principles and practice for segmentation, registration, and image analysis. Wellesley: A K Peters.

    Google Scholar 

  • Yu, F., & Ip, H. H. (2008). Semantic content analysis and annotation of histological images. Computers in Biology and Medicine, 38(6), 635–649.

    PubMed  Google Scholar 

  • Zalis, M. E., Barish, M. A., et al. (2005). CT colonography reporting and data system: A consensus proposal. Radiology, 236(1), 3–9.

    PubMed  Google Scholar 

  • Zhang, Y. Y., Brady, M., et al. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45–57.

    PubMed  CAS  Google Scholar 

  • Zhenyu, H., Yanjie, Z., et al. (2009). Combining text retrieval and content-based image retrieval for searching a large-scale medical image database in an integrated RIS/PACS environment, SPIE.

    Google Scholar 

  • Zijdenbos, A. P., Evans, A. C., et al. (1996). Automatic quantification of multiple sclerosis lesion volume using stereotactic space. Proc. In 4th International conference on visualization in biomedical computing. Hamburg (pp. 439–448).

    Google Scholar 

  • Zijdenbos, A. P., Evans, A. C., et al. (1996). Automatic quantification of multiple sclerosis lesion volume using stereotactic space. Proc. In 4th International conference on visualization in biomedical computing. Hamburg (pp. 439–448).

    Google Scholar 

  • Horii, S.C. (1996). Image acquisition: Sites, technologies and approaches. In Greenes, R.A. and Bauman, R.A. (eds.) Imaging and information management: computer systems for a changing health care environment. The Radiology Clinics of North America, 34(3):469–494.

    CAS  Google Scholar 

  • Foley, D.D., Van Dam, A., Feiner, S.K., Hughes, J.F. (1990). Computer Graphics: Principles and Practice. Reading, MA: Addison-Wesley.

    Google Scholar 

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Rubin, D.L., Greenspan, H., Brinkley, J.F. (2014). Biomedical Imaging Informatics. In: Shortliffe, E., Cimino, J. (eds) Biomedical Informatics. Springer, London. https://doi.org/10.1007/978-1-4471-4474-8_9

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