Soft Computing based Retrieval System for Medical Applications

With increasing data in medical databases, medical data retrieval is growing in popularity. Some of this analysis including inducing propositional rules from databases using many soft techniques, and then using these rules in an expert system. Diagnostic rules and information on features are extracted from clinical databases on diseases of congenital anomaly. This paper explain the latest soft computing techniques and some of the adaptive techniques encompasses an extensive group of methods that have been applied in the medical domain and that are used for the discovery of data dependencies, importance of features, patterns in sample data, and feature space dimensionality reduction. These approaches pave the way for new and interesting avenues of research in medical imaging and represent an important challenge for researchers.

Authors:

• Pardeep Singh
• Sanjay Sharma

Keywords:

• CBIR
• GA
• Rough sets
• CBMIR
• SVM.

References:

[1] J. PETERS, "NEAR SETS. SPECIAL THEORY ABOUT
NEARNESS OF OBJECTS," FUNDAMENTAL INFORMATICAE,
VOL. 75, NO. 1-4, PP. 407-433, 2007.
[2] Near sets. toward approximation space-based object recognition,"
Lecture Notes in Artificial Intelligence, vol. 4481, pp. 22-33, 2007.
[3] Near sets. general theory about nearness of objects," Applied
Mathematical Sciences, vol. 1, no. 53, pp. 2609-2029, 2007.
[4] F. Idris, S. Panchanathan, Review of image and video indexing
techniques, J. Visual Commun. Image Representation 8 (2)(1997)
146}166.
[5] Y. Rui et al., Image retrieval current techniques, promising directions,
and open issues, J. Visual Commun. Image Representat. 10 (1)(1999)
39}62.
[6] M. de Marsicoi et al., Indexing pictorial documents by their content: a
survey of current techniques, Image and Vision Comput. 15
(1997)119 }141.
[7] M.J. Swain, Color indexing, Int. J. Comput. Vision 7 (1)(1991)
11}32.
[8] J.R. Smith, S.F. Chang, Querying by color regions using the
VisualSEEk content-based visual query system, in: M.T. Maybury
(Ed.), Intelligent Multimedia Information Retrieval, AAAI Press,
Menlo Park, CA, 1997.
[9] F. Liu, R.W. Picard, Periodicity directionality and randomness:
World features for image modelling and retrieval, IEEE. 722-733.
[10] B.S. Manjunath, W.Y. Ma, Texture features for browsing and
retrieval of large image data, IEEE Trans. Pattern Anal. Mach.
Intell.837 -842.
[11] R. Mehrotra, J.E. Gary, Similar shape retrieval in shape data
management, IEEE Comput. 28 (9)57 }62, Sep 1995.
[12] A.K. Jain, A. Vailaya, Image retrieval using color and shape, Pattern
Recognition 29 (8)(1996) 1233}1244.
[13] V.N. Gudivada, V.V. Raghavan, Design and evaluation of algorithms
for image retrieval by spatial similarity, ACM (1995) 115-144.
[14] Rough Sets And Near Sets In Medical Imaging A Review Aboul Ella
Hassanien_, Ajith Abraham, Ieee Trans. On Information Technology
In Biomedicine, Vol. Xx, No. Xx, Xxx. 2009
[15] C.-B. Chena and L.-Y. Wang, "Rough set-based clustering with
refinement using shannon-s entropy theory," Computers and
Mathematics with Applications, vol. 52, no. 10-11, pp. 1563-1576,
2006.
[16] V.N. Gudivada, V.V. Raghavan, Content-based image retrieval
systems, IEEE Comput. 28 (9)(1995) 18-22.
[17] J.P. Eakins, M.E Graham, Content-based image retrieval, JISC
Technology Applications Programme Report 39, October 1999.
Available online at http://www.unn.ac.uk/ iidr/CBIR/report.html.
[18] L. Armitage, P.G.B. Enser, Analysis of user need in image archives,
J. Inform. Sci. 23 (4)(1997) 287-299.
[19] J.P. Eakins, Techniques for image retrieval, Library and Information
Briefings 85, British Library and South Bank University, London,
1998.
[20] G. Salton, The SMART retrieval system * experiments in automatic
document processing, Prentice-Hall, New Jersey, 1971.
[21] Y. Rui et al., `Relevance feedback techniques in interactive contentbased
image retrievala in: I.K. Sethi, R.C. Jain (Eds.), Storage and
Retrieval for Image and Video Databases VI, Proc SPIE 3312, 1997,
pp. 25}36.
[22] C. Meilhac et al., `Relevance feedback in Surfimagea Proceedings of
Fourth IEEE Workshop on Applications of Computer Vision
(WACV'98), 1998, pp. 266-267.
[23] N. Beckmann, The R-tree: an e$cient and robust access method for
points and rectangles,ACM SIGMOD Record 19 (2)(1990) 322-331.
[24] M. Wertheimer, `Untersuchungen zur Lehre von der Gestalta,
Psycholog. Forschung 4 (1923)301 }350. (Translated as laws of
organization in perceptual forms, in: W.D. Ellis (Ed.), A Sourcebook
of Gestalt Psychology, Humanities Press, New York, 1950).
[25] G.F. Luger, W.A. Stubble"eld, Arti"cial Intelligence, Addison-
Wesley, Reading, MA, 1997.