Commenced in January 2007
Paper Count: 30184
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079570Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2253
 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 .
 R.S. Torres, A.X. Falc├úo and L. da F. Costa, A graph-based approach for multiscale shape analysis, Pattern Recognition 37 (6) (2004), pp. 1163-1174.
 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.
 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.
 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
 M.S. Lew (Ed.), Principles of Visual Information RetrievalÔÇö Advances in Pattern Recognition, Springer, London/Berlin/Heidelberg, 2001.
 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.
 J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA (1992).
 B. Bhanu and Y. Lin, Object detection in multi-modal images using genetic programming, Appl. Soft Comput. 4 (2) (2004), pp. 175-201
 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
 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.
 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.
 J.H. Holland, Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, MA (1992).
 W.B. Langdon, Data Structures and Genetic Programming: Genetic Programming+Data Structures=Automatic Programming!, Kluwer Academic Publishers, Dordrecht (1998).
 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).
 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.
 I.L. Dryden, K.V. Mardia, Statistical Shape Analysis, John Wiley & Sons Ltd., West Sussex, England, 1998.
 David R. Martin, Charless Fowlkes, and Jitendra Malik. Learning to detect natural image boundaries using brightness and texture. In NIPS, pages 1255-1262, 2002.
 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.
 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
 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.
 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.
 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.
 H. Muller, A. Geissbuhler, S. Marchand-Maillet, and P. Clough. Benchmarking image retrieval applications. In Visual Information Systems, 2004.
 Henning Muller, Wolfgang M┬¿uller, St'ephane Marchand-Maillet, David McG. Squire, and Thierry Pun. A framework for benchmarking in visual information retrieval, 2003.
 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.
 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
 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.
 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.
 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.
 Cordelia Schmid and Roger Mohr. Local grayvalue invariants for image retrieval. IEEE Trans Pattern Anal. Mach. Intell., 19(5):530- 535, 1997.
 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.