Commenced in January 2007
Paper Count: 32578
Soft Computing based Retrieval System for Medical Applications
Abstract: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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079812Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1621
 J. PETERS, "NEAR SETS. SPECIAL THEORY ABOUT NEARNESS OF OBJECTS," FUNDAMENTAL INFORMATICAE, VOL. 75, NO. 1-4, PP. 407-433, 2007.
 Near sets. toward approximation space-based object recognition," Lecture Notes in Artificial Intelligence, vol. 4481, pp. 22-33, 2007.
 Near sets. general theory about nearness of objects," Applied Mathematical Sciences, vol. 1, no. 53, pp. 2609-2029, 2007.
 F. Idris, S. Panchanathan, Review of image and video indexing techniques, J. Visual Commun. Image Representation 8 (2)(1997) 146}166.
 Y. Rui et al., Image retrieval current techniques, promising directions, and open issues, J. Visual Commun. Image Representat. 10 (1)(1999) 39}62.
 M. de Marsicoi et al., Indexing pictorial documents by their content: a survey of current techniques, Image and Vision Comput. 15 (1997)119 }141.
 M.J. Swain, Color indexing, Int. J. Comput. Vision 7 (1)(1991) 11}32.
 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.
 F. Liu, R.W. Picard, Periodicity directionality and randomness: World features for image modelling and retrieval, IEEE. 722-733.
 B.S. Manjunath, W.Y. Ma, Texture features for browsing and retrieval of large image data, IEEE Trans. Pattern Anal. Mach. Intell.837 -842.
 R. Mehrotra, J.E. Gary, Similar shape retrieval in shape data management, IEEE Comput. 28 (9)57 }62, Sep 1995.
 A.K. Jain, A. Vailaya, Image retrieval using color and shape, Pattern Recognition 29 (8)(1996) 1233}1244.
 V.N. Gudivada, V.V. Raghavan, Design and evaluation of algorithms for image retrieval by spatial similarity, ACM (1995) 115-144.
 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
 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.
 V.N. Gudivada, V.V. Raghavan, Content-based image retrieval systems, IEEE Comput. 28 (9)(1995) 18-22.
 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.
 L. Armitage, P.G.B. Enser, Analysis of user need in image archives, J. Inform. Sci. 23 (4)(1997) 287-299.
 J.P. Eakins, Techniques for image retrieval, Library and Information Briefings 85, British Library and South Bank University, London, 1998.
 G. Salton, The SMART retrieval system * experiments in automatic document processing, Prentice-Hall, New Jersey, 1971.
 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.
 C. Meilhac et al., `Relevance feedback in Surfimagea Proceedings of Fourth IEEE Workshop on Applications of Computer Vision (WACV'98), 1998, pp. 266-267.
 N. Beckmann, The R-tree: an e$cient and robust access method for points and rectangles,ACM SIGMOD Record 19 (2)(1990) 322-331.
 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).
 G.F. Luger, W.A. Stubble"eld, Arti"cial Intelligence, Addison- Wesley, Reading, MA, 1997.