Skip to main content

Advertisement

SpringerLink
Log in

POSTGRESQL-IE: An Image-handling Extension for PostgreSQL

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

The last decade witnessed a growing interest in research on content-based image retrieval (CBIR) and related areas. Several systems for managing and retrieving images have been proposed, each one tailored to a specific application. Functionalities commonly available in CBIR systems include: storage and management of complex data, development of feature extractors to support similarity queries, development of index structures to speed up image retrieval, and design and implementation of an intuitive graphical user interface tailored to each application. To facilitate the development of new CBIR systems, we propose an image-handling extension to the relational database management system (RDBMS) PostgreSQL. This extension, called PostgreSQL-IE, is independent of the application and provides the advantage of being open source and portable. The proposed system extends the functionalities of the structured query language SQL with new functions that are able to create new feature extraction procedures, new feature vectors as combinations of previously defined features, and new access methods, as well as to compose similarity queries. PostgreSQL-IE makes available a new image data type, which permits the association of various images with a given unique image attribute. This resource makes it possible to combine visual features of different images in the same feature vector. To validate the concepts and resources available in the proposed extended RDBMS, we propose a CBIR system applied to the analysis of mammograms using PostgreSQL-IE.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig 1
Fig 2
Fig. 3
Fig 4
Fig 5
Fig 6

Similar content being viewed by others

References

  1. Datta R, Li J, Wang JZ: Content-based image retrieval—approaches and trends of the new age. In: Proceedings of the ACM International Workshop on Multimedia Information Retrieval, ACM Multimedia, Singapore, November, 2005, pp 253–261

  2. Guliato D, Rangayyan RM, Carvalho JD, Santiago SA: Spiculation-preserving polygonal modeling of contours of breast tumors. In: Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, New York City, NY, September, 2006, pp 2791–2794

  3. Rangayyan RM, Guliato D, Carvalho JD, Santiago SA: Feature extraction from the turning angle function for the classification of breast tumors. In Proceedings of the International Special Topics Conference on Information Technology in Biomedicine—IEEE ITAB2006, Ioannina, Greece, October, 2006 (6 pages on CDROM)

  4. Carvalho JD, Rangayyan RM, Guliato D, Santiago SA: Polygonal modeling of contours using the turning angle function. In 20th IEEE Canadian Conference on Electrical and Computer Engineering, Vancouver, BC, April, 2007, pp 1090–1267

  5. Chen Y, Wang JZ: A region-based fuzzy feature matching approach to content-based image retrieval. IEEE Trans Pattern Anal Mach Intell 24(9):1252–1267, 2002

    Article  Google Scholar 

  6. Mudigonda NR, Rangayyan RM, Desautels JEL: Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans Med Imag 20(12):1215–1227, 2001

    Article  CAS  Google Scholar 

  7. Rangayyan RM, Nguyen TM: Fractal analysis of contours of breast masses in mammograms. J Digit Imaging 20(3):223–237, 2007 (September)

    Article  PubMed  Google Scholar 

  8. Veltkamp RC, Tanase M: Content-based Image and Video Retrieval, Norwell, MA: Kluwer, 2002

    Google Scholar 

  9. Csillaghy A, Hinterberger H, Benz AO: Content-based image retrieval in astronomy. Inf Retr 3(3):229–241, 2000

    Article  Google Scholar 

  10. Painter TH, Dozier J, Roberts DA, Davis RE, Green RO: Retrieval of subpixel snowcovered area and grain size from imaging spectrometer data. Remote Sens Environ 85(1):64–77, 2003

    Article  Google Scholar 

  11. Schroder M, Rehrauer H, Seidel K, Datcu M: Interactive learning and probabilistic retrieval in remote sensing images archives. IEEE Trans Geosci Remote Sens 38(5):2288–2298, 2000

    Article  Google Scholar 

  12. Wang JZ, Li J, Wiederhold J: SIMPLIcity: semantics sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23(9):947–963, 2001

    Article  Google Scholar 

  13. Guliato D, Rangayyan RM, Melo EV, Soares RC: A system for content-based image retrieval and analysis of mammograms using PostgreSQL with image-handling extension. In: Proceedings of the Fifth IASTED International Conference on Biomedical Engineering, Innsbruck, Austria, February, 2007, pp 402–408

  14. Alto H, Rangayyan RM, Paranjape RB, Desautels JEL, Bryant H: An indexed atlas of digital mammograms for computer-aided diagnosis of breast cancer. Ann Telecommun 58(5–6):820–835, 2003

    Google Scholar 

  15. Baroni MCN, Rezende HL, Traina-Jr C, and Traina AJM: Queryng complex objects by similarity in SQL*. In: Proceedings of Brazilian Symposium on Databases—SBBD2005, Uberlândia, MG, Brazil, October–November, 2005, pp 1–5

  16. Böhm C, Berchtold S, Keim DA: Searching in high-dimensional spaces—index structures for improving the performance of multimedia databases. ACM Comput Surv 33(3):322–373, 2001

    Article  Google Scholar 

  17. Chávez E, Navarro G, Baeza-Yates R, Marroquím J: Searching in metric space. ACM Comput Surv 33(3):273–321, 2001

    Article  Google Scholar 

  18. Ciaccia P, Patella M: M-tree: an efficient access method for similarity search in metric spaces. In: Proceedings of International Conference on Very Large Data Bases (VLDB), Athens, Greece, 1997, pp 426–435

  19. Traina Jr, C, Traina AJM, Faloutsos C, Seeger B: Fast indexing and visualization of metric datasets using slim-trees. IEEE Trans Knowl Data Eng 14(2):244–260, 2002

    Article  Google Scholar 

  20. Digout C, Nascimento M, Coman A: Similarity search and dimensionality reduction: not all dimensions are equally useful. In: Proceedings of Database Systems for Advanced Applications—9th International Conference, DASFAA 2004, Jeju Island, Korea, March 17–19. Springer, Berlin, Germany, 2004 (also in Lect Notes Comput Sci 2973:831–842)

  21. Kailing K, Kriegel HP, Schönauer S, Seidl T: Efficient similarity search for hierarquical data in large databases. In: Proceedings of Advances in Database Technology—EDBT 2004—9th International Conference on Extending Database Technology, Heraklion, Crete, Greece, March. Springer, Berlin, Germany, 2004 (also in Lect Notes Comput Sci 2992:676–693)

  22. Vieira MR, Chino F, Traina-Jr, C, Traina AJM: Dbm-tree: a metric access method sensitive to local density data. In: Proceedings of Brazilian Symposium on Databases—SBBD2004, Brasilia, DF, Brazil, October, 2004, pp 163–177

  23. Carey MJ, Kossmann D: On saying enough already in SQL. In: Proceedings of the 1997 ACM SIGMOD international conference on Management of data, Tucson, AZ, May, 1997, pp 219–230

  24. Carey MJ, Kossmann D: Reducing the braking distance of an SQL query engine. In: Proceedings of the Conf. on Very Large Data Bases (VLDB), New York City, NY. VLDB Endowment, Saratoga, CA, 1998, pp 158–169

  25. Gao L, Wang M, Wang XS, Padmanabhan S: Expressing and optimizing similarity queries in SQL. In: Proceedings of Conceptual Modeling—ER—23rd International Conference on Conceptual Modeling, Shanghai, China, November. Springer, Berlin, Germany, 2004 (also in Lect Notes Comput Sci 3288:464–478)

  26. Melton J, Eisenberg A: SQL multimedia and application packages (SQL/MM). ACM SIGMOD Record 30(4):97–102, 2001 (December)

    Article  Google Scholar 

  27. The POSTGRES Group: The POSTGRES Reference Manual, Berkeley, CA: Computer Science Division, University of California, 1993 (January)

    Google Scholar 

  28. Guliato D, Rangayyan RM, Carvalho JD, Santiago SA: Polygonal modeling of contours of breast tumors with the preservation of spicules. IEEE Trans Biomed Eng 55:14–20, 2008

    Article  PubMed  Google Scholar 

  29. Melo EV, Guliato D, Rangayyan RM, Soares RS: SISPRIM—Sistema De Pesquisa Com Suporte Para Recupera, cão de imagens por conte’udo. In: Proceedings of WIM2006 - VI Workshop de Inform’atica M’edica, Vila Velha, ES, Brazil, June, 2006.

  30. IBM: DB2 Universal Database Image, Audio, and Video Extenders Administration and Programming. 2000. http://www-306.ibm.com/software/data/db2/extenders/index.html

  31. Informix: Excalibur Image Datablade Module, Users Guide. 2000. http://informix.com.ua/answers/english/alpha.htm

  32. Informix: Informix Image Foundation DataBlade Module, Users Guide, Version 2.0. 2000 (December)

  33. Oracle: Oracle8i interMedia Audio, Image, and Video—Users Guide and Reference. 2005. http://download.oracle.com/docs/pdf/A67296_01.pdf

  34. ISO: ISO/IEC IS 13249-5:2001 SQL/MM, Information Technology Database Languages SQL Multimedia and Application Packages Part 5: Still Image. 2001.

  35. Stolze K: Still image extensions in database systems—a product overview. In: Datenbank-Spektrum, 2002, pp 40–47 (February)

  36. The POSTGRES Group. PostgreSQL 8.0.0 Documentation. 2005

  37. Rangayyan RM, El-Faramawy NM, Desautels JEL, Alim OA: Measures of acutance and shape for classification of breast tumors. IEEE Trans Med Imag 16(6):799–810, 1997

    Article  CAS  Google Scholar 

  38. Guliato D, Bôaventura RS, Melo EV, Rangayyan RM: AMDI: an indexed atlas of digital mammograms that integrates case studies, e-learning, and research systems via the web. In: Suri JS, Rangayyan RM Eds. Recent Advances in Breast Imaging, Mammography, and Computer-aided Diagnosis of Breast Cancer. Bellingham, WA: SPIE, 2006, pp. 529–555

    Google Scholar 

  39. American College of Radiology: Breast Imaging Reporting and Data System BI-RADS, 4th edition. Reston, VA: American College of Radiology, 2004

    Google Scholar 

  40. Screen Test: Alberta Program for the Early Detection of Breast Cancer—2001/03 Biennial Report. 2004. http://www.cancerboard.ab.ca/screentest

  41. Alto H, Rangayyan RM, Desautels JEL: Content-based retrieval and analysis of mammographic masses. J Electron Imaging 14(2):023016, 2005

    Article  Google Scholar 

  42. The Mammographic Image Analysis Society digital mammogram database. http://peipa.essex.ac.uk/info/mias.html, accessed October, 2006

  43. Rangayyan RM, Mudigonda NR, Desautels JEL: Boundary modelling and shape analysis methods for classification of mammographic masses. Med Biol Eng Comput 38:487–496, 2000

    Article  PubMed  CAS  Google Scholar 

  44. Digital Database for Screening Mammography. http://marathon.csee.usf.edu/Mammography/Database.html, accessed June, 2007.

  45. Rui Y, Yang TS, Mehrotra S: Content-based Image Retrieval with Relevance Feedback in Mars. In: IEEE International Conference in Image Processing, volume 2, Santa Barbara, CA, 1997, pp 815–818

  46. Rui Y, Yang TS, Ortega M, Mehrotra S: Relevance feedback: a powerful tool in interactivecontent-based image retrieval. IEEE Trans Circuits Syst Video Technol 8(5):644–655, 1998 (September)

    Article  Google Scholar 

  47. Triana AJM, Marques J, Traina Jr C. Fighting the semantic gap on CBIR system through new relevance feedback techniques. In: Proceedings of 19th IEEE Symposium on Computer-based Medical Systems (CBMS’06), 2006, pp 881–886

  48. Guliato D, de Melo EV, Bôaventura RS, Janones FR, de Deus V, Rangayyan RM: AMDI: an atlas to integrate case studies, e-learning, and research systems via the Web. In: Proceedings of the IASTED International Conference on Telehealth. Banff, AB, Canada, 2005, pp 69–74

  49. Guliato D, Caetano M, Janones FR, de Deus V, Lima SC, Rangayyan RM, Bôaventura RS, and Marques PMA. AMDI: An indexed atlas of digital mammograms availablevia the Web. In: III Latin American Congress on Biomedical Engineering, IFMBE Proceedings, 5, 2004 (4 pages on CDROM)

Download references

Acknowledgment

This work was supported by the Conselho Nacional Desenvolvimento Científico e Tecnológico, Brazil, Universidade Federal de Uberlândia, Brazil, and Research Services, University of Calgary, Canada.

Author information

Authors and Affiliations

  1. Faculdade de Computação, Universidade Federal de Uberlândia, Av. João Naves de Ávila, 2121, 38.400-902, Minas Gerais, Brazil

    Denise Guliato, Ernani V. de Melo & Robson C. Soares

  2. Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive N. W., Calgary, Alberta, Canada, T2N 1N4

    Rangaraj M. Rangayyan

Authors
  1. Denise Guliato
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ernani V. de Melo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rangaraj M. Rangayyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robson C. Soares
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Denise Guliato.

Appendix A

Appendix A

A1—Syntax for SQL-IE Data Definition Functions

In this Appendix, we present the detailed syntax of the definition functions used in “Data Definition Functions.”

  • The Create_Extractor function

  • The Define_Feature_Vector function

  • The Create_AccessMethod function—The access method must be developed in the C programming language, converted to library format (dll or so), and has to include the following functions:

The input parameters for the similarity operators are: the score name, the value of the neighborhood for KNN and the value of ratio for the RANGE operators, the name of the index structure, the image class, and the file path of the reference image for the similarity query.

A2—Syntax for SQL-IE Manipulation Commands

In this Appendix, we present the detailed syntax of the manipulation functions used in “Data Manipulation Functions.”

  • The Insert_Image function

  • The Set_Feature_Vector function

Rights and permissions

Reprints and permissions

About this article

Cite this article

Guliato, D., de Melo, E.V., Rangayyan, R.M. et al. POSTGRESQL-IE: An Image-handling Extension for PostgreSQL. J Digit Imaging 22, 149–165 (2009). https://doi.org/10.1007/s10278-007-9097-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-007-9097-5

Key words

Search

Navigation