Date of Publication :7th December 2015
Abstract: Biomedical imaging is becoming increasingly important as an approach to synthesize, extract and translate useful information from large multidimensional databases accumulated in research frontiers such as functional genomics, proteomics, and functional imaging. Image mining is rapidly gaining attention in the field of data mining, information retrieval and multimedia databases because of its potential in discovering useful image patterns based on color, texture, shape and basic descriptors of any image. Image mining system can help in reducing the time lag with the results as well as dependency on observations by naked human eye. This paper explains the algorithm for detecting pathogenic cells in renal tissue.
Reference :
-
[1] H. Wynne, L.L Mong, and J. Zhang, “Image mining: trends and developments. Journal of Intelligent Information Systems”, 2002
[2] http ://www.nps.org.au/medical-tests/medicalimaging/for-individuals/advantages-anddisadvantages
[3] T. Y. Gajjar, N. C. Chauhan, “A Review on Image Mining Frameworks and Techniques”, International Journal of Computer Science and Information Technologies
[4] Prabhjeet Kaur, Kamaljit Kaur, “Review of Different Existing Image Mining Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering Research Paper, Volume 4, Issue 6, June 2014
[5] A.Kannan, Dr.V.Mohan, Dr.N.Anbazhagan “An Effective Method of Image Retrieval using Image Mining Techniques”, The International journal of
[12] Data Mining in Biomedical Imaging, Signaling, and Systems, Sumeet Dua, Rajendra Acharya U [13] Medical Imaging Physics, William R. Hendee, E. Russell Ritenour.
[14] The Computational Challenges of Medicals Imaging, Michael Brenner, Alvin Despain, Robert Hederson, Darrel Long, William Press, John Tony, Peter Weinberge.