A Medical Images Based Retrieval System using Soft Computing Techniques
Content-Based Image Retrieval (CBIR) has been
one on the most vivid research areas in the field of computer vision
over the last 10 years. Many programs and tools have been
developed to formulate and execute queries based on the visual or
audio content and to help browsing large multimedia repositories.
Still, no general breakthrough has been achieved with respect to
large varied databases with documents of difering sorts and with
varying characteristics. Answers to many questions with respect to
speed, semantic descriptors or objective image interpretations are
still unanswered. In the medical field, images, and especially
digital images, are produced in ever increasing quantities and used
for diagnostics and therapy. In several articles, content based
access to medical images for supporting clinical decision making
has been proposed that would ease the management of clinical data
and scenarios for the integration of content-based access methods
into Picture Archiving and Communication Systems (PACS) have
been created. This paper gives an overview of soft computing
techniques. New research directions are being defined that can
prove to be useful. Still, there are very few systems that seem to be
used in clinical practice. It needs to be stated as well that the goal
is not, in general, to replace text based retrieval methods as they
exist at the moment.
[1] A Genetic Programming Framework For Content-Based Image
Retrieval Ricardo Da, Alexandre, Marcos, Pattern Recognition
Volume 42, Issue 2, February 2009, Pages 283-292 Elsevier .
[2] R.S. Torres, A.X. Falcão and L. da F. Costa, A graph-based approach
for multiscale shape analysis, Pattern Recognition 37 (6) (2004), pp.
1163-1174.
[3] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta and R. Jain,
Content-based image retrieval at the end of the years, IEEE TPAMI
22 (12) (2000), pp. 1349-1380.
[4] N. Arica and F.T.Y. Vural, BAS: a perceptual shape descriptor based
on the beam angle statistics, Pattern Recognition Lett. 24 (9-10)
(2003), pp. 1627-1639.
[5] D. Tao, X. Tang and X. Li, Which components are important for
interactive image searching, IEEE Trans. Circuits Syst. Video
Technol. 18 (1) (2008), pp. 3-11
[6] M.S. Lew (Ed.), Principles of Visual Information RetrievalÔÇö
Advances in Pattern Recognition, Springer,
London/Berlin/Heidelberg, 2001.
[7] H. Shao, J.-W. Zhang, W.C. Cui, H. Zhao, Automatic feature weight
assignment based on genetic algorithm for image retrieval, in: IEEE
International Conference on Robotics, Intelligent Systems and Signal
Processing, 2003, pp. 731-735.
[8] J.R. Koza, Genetic Programming: On the Programming of Computers
by Means of Natural Selection, MIT Press, Cambridge, MA (1992).
[9] B. Bhanu and Y. Lin, Object detection in multi-modal images using
genetic programming, Appl. Soft Comput. 4 (2) (2004), pp. 175-201
[10] W. Fan, E.A. Fox, P. Pathak and H. Wu, The effects of fitness
functions on genetic programming-based ranking discovery for web
search, JASIST 55 (7) (2004), pp. 628-636
[11] B. Zhang, Intelligent fusion of structural and citation-based evidence
for text classification, Ph.D. Thesis, Department of Computer
Science, Virginia Polytechnic Institute and State University, 2006.
[12] Z. Stejić, Y. Takama and K. Hirota, Mathematical aggregation
operators in image retrieval: effect on retrieval performance and role
in relevance feedback, Signal Processing 85 (2) (2005), pp. 297-324.
[13] J.H. Holland, Adaptation in Natural and Artificial Systems, MIT
Press, Cambridge, MA (1992).
[14] W.B. Langdon, Data Structures and Genetic Programming: Genetic
Programming+Data Structures=Automatic Programming!, Kluwer
Academic Publishers, Dordrecht (1998).
[15] W. Banzhaf, P. Nordin, R.E. Keller and F.D. Francone, Genetic
ProgrammingÔÇöAn Introduction: On the Automatic Evolution of
Computer Programs and its Applications, Morgan Kaufmann, San
Francisco, CA (1998).
[16] H. Muller, N. Michoux, D. Bandon, A. Geissbuhler, "A Review of
Content_based Image Retrieval Systems in Medical Application -
Clinical Benefits and Future Directions", Int J Med Inform, 73(1),
2004, pp. 1-23.
[17] I.L. Dryden, K.V. Mardia, Statistical Shape Analysis, John Wiley &
Sons Ltd., West Sussex, England, 1998.
[18] David R. Martin, Charless Fowlkes, and Jitendra Malik. Learning to
detect natural image boundaries using brightness and texture. In
NIPS, pages 1255-1262, 2002.
[19] David R. Martin, Charless Fowlkes, and Jitendra Malik. Learning to
detect natural image boundaries using local brightness, color, and
texture cues. IEEE Trans. Pattern Anal. Mach. Intell.,26(5):530-549,
2004.
[20] K. Mikolajczyk and C. Schmid. A performance evaluation of local
descriptors. IEEE Trans Pattern Recognition and Machine
Intelligence, pages 1615-1630, October 2005. 2, 5
[21] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F.
Schaffalitzky, T. Kadir, and L. Van Gool. A comparison of affine
region detectors. Accepted in International Journal of Computer
Vision, 2005.
[22] P-A. Mo¨ellic, P. H`ede, G. Grefenstette, and C. Millet. Evaluating
content based image retrieval techniques with the one million images
clic testbed. In Proc World Enformatika Congress, pages171-174,
Istanbul, Turkey, February 25-27 2005.
[23] H. Muller, P. Clough, A. Geissbuhler, and W. Hersh. Imageclef 2004-
2005: results, experiences and new ideas for image retrieval
evaluation. In Proceedings of the Fourth International Workshop on
Content-Based Multimedia Indexing (CBMI2005), to appear, Riga,
Latvia, 2005.
[24] H. Muller, A. Geissbuhler, S. Marchand-Maillet, and P. Clough.
Benchmarking image retrieval applications. In Visual Information
Systems, 2004.
[25] Henning Muller, Wolfgang M¨uller, St'ephane Marchand-Maillet,
David McG. Squire, and Thierry Pun. A framework for benchmarking
in visual information retrieval, 2003.
[26] Wolfgang Muller, St'ephane Marchand-Maillet, Henning M¨uller,
and Thierry Pun. Towards a fair benchmark for image browsers. In
SPIE Photonics East, Voice, Video, and Data Communications,
Boston, MA, USA, 5-8 2000.
[27] R. Fergus, P. Perona, and A. Zisserman. Object class recognition by
unsupervised scale-invariant learning. In Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition, volume 2,
pages 264-271, Madison, Wisconsin, June 2003
[28] Andreas Opelt, Michael Fussenegger, Axel Pinz, and Peter Auer.
Weak hypotheses and boosting for generic object detection and
recognition. In ECCV (2), pages 71-84, 2004.
[29] Paul Over, Clement H. C. Leung, Horace Ho-Shing Ip, and Micheal
Grubinger. Multimedia retrieval benchmarks. IEEE Multimedia,
11(2):80-84, April-June 2004.
[30] F. Rothganger, S. Lazebnik, C. Schmid, and J. Ponce. 3d object
modeling and recognition using affine-invariant patches and multiview
spatial constraints. In CVPR (2), pages 272-280, 2003.
[31] Cordelia Schmid and Roger Mohr. Local grayvalue invariants for
image retrieval. IEEE Trans Pattern Anal. Mach. Intell., 19(5):530-
535, 1997.
[32] C.D. Ferreira, R.S. Torres, Image retrieval with relevance feedback
based on genetic programming, Technical Report IC-07-05, Institute
of Computing, State University of Campinas, Feburary 2007.
[1] A Genetic Programming Framework For Content-Based Image
Retrieval Ricardo Da, Alexandre, Marcos, Pattern Recognition
Volume 42, Issue 2, February 2009, Pages 283-292 Elsevier .
[2] R.S. Torres, A.X. Falcão and L. da F. Costa, A graph-based approach
for multiscale shape analysis, Pattern Recognition 37 (6) (2004), pp.
1163-1174.
[3] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta and R. Jain,
Content-based image retrieval at the end of the years, IEEE TPAMI
22 (12) (2000), pp. 1349-1380.
[4] N. Arica and F.T.Y. Vural, BAS: a perceptual shape descriptor based
on the beam angle statistics, Pattern Recognition Lett. 24 (9-10)
(2003), pp. 1627-1639.
[5] D. Tao, X. Tang and X. Li, Which components are important for
interactive image searching, IEEE Trans. Circuits Syst. Video
Technol. 18 (1) (2008), pp. 3-11
[6] M.S. Lew (Ed.), Principles of Visual Information RetrievalÔÇö
Advances in Pattern Recognition, Springer,
London/Berlin/Heidelberg, 2001.
[7] H. Shao, J.-W. Zhang, W.C. Cui, H. Zhao, Automatic feature weight
assignment based on genetic algorithm for image retrieval, in: IEEE
International Conference on Robotics, Intelligent Systems and Signal
Processing, 2003, pp. 731-735.
[8] J.R. Koza, Genetic Programming: On the Programming of Computers
by Means of Natural Selection, MIT Press, Cambridge, MA (1992).
[9] B. Bhanu and Y. Lin, Object detection in multi-modal images using
genetic programming, Appl. Soft Comput. 4 (2) (2004), pp. 175-201
[10] W. Fan, E.A. Fox, P. Pathak and H. Wu, The effects of fitness
functions on genetic programming-based ranking discovery for web
search, JASIST 55 (7) (2004), pp. 628-636
[11] B. Zhang, Intelligent fusion of structural and citation-based evidence
for text classification, Ph.D. Thesis, Department of Computer
Science, Virginia Polytechnic Institute and State University, 2006.
[12] Z. Stejić, Y. Takama and K. Hirota, Mathematical aggregation
operators in image retrieval: effect on retrieval performance and role
in relevance feedback, Signal Processing 85 (2) (2005), pp. 297-324.
[13] J.H. Holland, Adaptation in Natural and Artificial Systems, MIT
Press, Cambridge, MA (1992).
[14] W.B. Langdon, Data Structures and Genetic Programming: Genetic
Programming+Data Structures=Automatic Programming!, Kluwer
Academic Publishers, Dordrecht (1998).
[15] W. Banzhaf, P. Nordin, R.E. Keller and F.D. Francone, Genetic
ProgrammingÔÇöAn Introduction: On the Automatic Evolution of
Computer Programs and its Applications, Morgan Kaufmann, San
Francisco, CA (1998).
[16] H. Muller, N. Michoux, D. Bandon, A. Geissbuhler, "A Review of
Content_based Image Retrieval Systems in Medical Application -
Clinical Benefits and Future Directions", Int J Med Inform, 73(1),
2004, pp. 1-23.
[17] I.L. Dryden, K.V. Mardia, Statistical Shape Analysis, John Wiley &
Sons Ltd., West Sussex, England, 1998.
[18] David R. Martin, Charless Fowlkes, and Jitendra Malik. Learning to
detect natural image boundaries using brightness and texture. In
NIPS, pages 1255-1262, 2002.
[19] David R. Martin, Charless Fowlkes, and Jitendra Malik. Learning to
detect natural image boundaries using local brightness, color, and
texture cues. IEEE Trans. Pattern Anal. Mach. Intell.,26(5):530-549,
2004.
[20] K. Mikolajczyk and C. Schmid. A performance evaluation of local
descriptors. IEEE Trans Pattern Recognition and Machine
Intelligence, pages 1615-1630, October 2005. 2, 5
[21] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F.
Schaffalitzky, T. Kadir, and L. Van Gool. A comparison of affine
region detectors. Accepted in International Journal of Computer
Vision, 2005.
[22] P-A. Mo¨ellic, P. H`ede, G. Grefenstette, and C. Millet. Evaluating
content based image retrieval techniques with the one million images
clic testbed. In Proc World Enformatika Congress, pages171-174,
Istanbul, Turkey, February 25-27 2005.
[23] H. Muller, P. Clough, A. Geissbuhler, and W. Hersh. Imageclef 2004-
2005: results, experiences and new ideas for image retrieval
evaluation. In Proceedings of the Fourth International Workshop on
Content-Based Multimedia Indexing (CBMI2005), to appear, Riga,
Latvia, 2005.
[24] H. Muller, A. Geissbuhler, S. Marchand-Maillet, and P. Clough.
Benchmarking image retrieval applications. In Visual Information
Systems, 2004.
[25] Henning Muller, Wolfgang M¨uller, St'ephane Marchand-Maillet,
David McG. Squire, and Thierry Pun. A framework for benchmarking
in visual information retrieval, 2003.
[26] Wolfgang Muller, St'ephane Marchand-Maillet, Henning M¨uller,
and Thierry Pun. Towards a fair benchmark for image browsers. In
SPIE Photonics East, Voice, Video, and Data Communications,
Boston, MA, USA, 5-8 2000.
[27] R. Fergus, P. Perona, and A. Zisserman. Object class recognition by
unsupervised scale-invariant learning. In Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition, volume 2,
pages 264-271, Madison, Wisconsin, June 2003
[28] Andreas Opelt, Michael Fussenegger, Axel Pinz, and Peter Auer.
Weak hypotheses and boosting for generic object detection and
recognition. In ECCV (2), pages 71-84, 2004.
[29] Paul Over, Clement H. C. Leung, Horace Ho-Shing Ip, and Micheal
Grubinger. Multimedia retrieval benchmarks. IEEE Multimedia,
11(2):80-84, April-June 2004.
[30] F. Rothganger, S. Lazebnik, C. Schmid, and J. Ponce. 3d object
modeling and recognition using affine-invariant patches and multiview
spatial constraints. In CVPR (2), pages 272-280, 2003.
[31] Cordelia Schmid and Roger Mohr. Local grayvalue invariants for
image retrieval. IEEE Trans Pattern Anal. Mach. Intell., 19(5):530-
535, 1997.
[32] C.D. Ferreira, R.S. Torres, Image retrieval with relevance feedback
based on genetic programming, Technical Report IC-07-05, Institute
of Computing, State University of Campinas, Feburary 2007.
@article{"International Journal of Medical, Medicine and Health Sciences:61662", author = "Pardeep Singh and Sanjay Sharma", title = "A Medical Images Based Retrieval System using Soft Computing Techniques", abstract = "Content-Based Image Retrieval (CBIR) has been
one on the most vivid research areas in the field of computer vision
over the last 10 years. Many programs and tools have been
developed to formulate and execute queries based on the visual or
audio content and to help browsing large multimedia repositories.
Still, no general breakthrough has been achieved with respect to
large varied databases with documents of difering sorts and with
varying characteristics. Answers to many questions with respect to
speed, semantic descriptors or objective image interpretations are
still unanswered. In the medical field, images, and especially
digital images, are produced in ever increasing quantities and used
for diagnostics and therapy. In several articles, content based
access to medical images for supporting clinical decision making
has been proposed that would ease the management of clinical data
and scenarios for the integration of content-based access methods
into Picture Archiving and Communication Systems (PACS) have
been created. This paper gives an overview of soft computing
techniques. New research directions are being defined that can
prove to be useful. Still, there are very few systems that seem to be
used in clinical practice. It needs to be stated as well that the goal
is not, in general, to replace text based retrieval methods as they
exist at the moment.", keywords = "CBIR, GA, Rough sets, CBMIR", volume = "4", number = "7", pages = "287-6", }