MacRad: Radiology image resource with a case-based retrieval system

  • Robert T. Macura
  • Katarzyna J. Macura
Application Sessions
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1010)


We have compiled a case-based retrieval system for radiology, MacRad, that is structured around descriptors for radiology image findings. Our goal is to provide a feature-coded image resource that allows the user to formulate image content-based queries when searching for reference images. MacRad is implemented as a relational database with an image archive. Each image in the library is indexed according to its radiologic content. We structured an index for coding image content as a hierarchical image description index using the relational format. The hierarchical index of radiologic findings allows multilevel query formulation that depends upon the user's level of experience. The system uses rules to control the search direction within the case library and generate lists of diagnostic hypotheses for decision support. Rules are embedded within the database structure. At present, the case library consists of 300 cases and 3,000 images that present intracranial masses on skull X-rays, CTs, MRIs, and angiograms.


image database case-based retrieval hierarchical index neuroradiology 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Schmidt HG, Norman GR, Boshuizen HPA. A cognitive perspective on medical expertise: Theory and implications. Acad Med 1990; 65(10):611–621.Google Scholar
  2. 2.
    Pizer SM, ter Haar Romeny BM. Fundamental Properties of Medical Image Perception. J Digital Imaging 1991; 4(4): 213–225.Google Scholar
  3. 3.
    Macura KJ, Macura RT, Morstad BD. Digital Case Library: A Resourse for Teaching, Learning, and Decision Support in Radiology. RadioGraphics 1995; 15(1): 155–164.Google Scholar
  4. 4.
    Macura RT, Macura KJ, Toro VE, Binet EF, Trueblood JH, Ji K. Computerized Case-Based Instructional System for Computed Tomography and Magnetic Resonance Imaging of Brain Tumors. Invest Radiol 1994; 29: 497–506.Google Scholar
  5. 5.
    Macura RT, Macura KJ. The Usefulness of Decision Tables in Encoding of Medical Knowledge. In Proceedings of the Third Symposium of the International Association of Knowledge Engineers IAKE '92. Galthersburg, MD; IAKE, 1992: 263–269.Google Scholar
  6. 6.
    Binet EF, Trueblood JH, Macura KJ, Morstad BD, Macura RT, Finkbeiner RV. R3, Radiology Resource and Reviev: From Floppy Disk to CD-ROM. RadioGraphics 1995; (July issue in print).Google Scholar
  7. 7.
    Index for Radiological Diagnoses, 4th Edition. American College of Radiology 1992.Google Scholar
  8. 8.
    Swett HA, Fisher PR, Cohn AI, Miller PL, Mutalik PG. Expert System controlled Image Display. Radiology 1989; 172: 487–493.Google Scholar
  9. 9.
    Cohn AI, Miller PL, Fisher PR, Mutalik PG, Swett HA. Knowledge-Based Radiologic Image Retrieval Using Axes of Clinical Relevance. Comput Biomed Res 1990; 23: 199–221.Google Scholar
  10. 10.
    Bramble JM, Insana MF, Dwyer III SJ. Information Retrieval for Teaching Files: A Preliminary Study. J Digital Imag 1990; 3(3): 164–169.Google Scholar
  11. 11.
    Greenes RA, McClure RC, Pattison-Gordon E, Sato L. The Findings-Diagnosis Continuum: Implications for Image Descriptions and Clinical Databases. In Proceedings of the 16th Symposium on Computer Applications in Medical Care. New York, NY; McGraw-Hill Inc., 1992: 383–387.Google Scholar
  12. 12.
    Taira RK, Cardenas AF, Chu WW, Breant CM, Hall T. A Knowledge-based Multi-media Database System for Skeletal Radiology. In HU Lemke, K Inamura, CC Jaffe, R Felix (Eds.). Computer Assisted Radiology. Berlin, Germany; Springer-Verlag: 1993, 649–654.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Robert T. Macura
    • 1
  • Katarzyna J. Macura
    • 1
  1. 1.Medical Informatics Section, Department of RadiologyMedical College of GeorgiaAugustaUSA

Personalised recommendations