The week's pick
Reseach Article
Content based Image Retrievals for Brain Related Diseases
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 85 - Number 11 |
| Year of Publication: 2014 |
| Authors: T. V. Madhusudhana Rao, S. Pallam Setty, Y. Srinivas |
10.5120/14883-3317
|
T. V. Madhusudhana Rao, S. Pallam Setty, Y. Srinivas . Content based Image Retrievals for Brain Related Diseases. International Journal of Computer Applications. 85, 11 ( January 2014), 6-10. DOI=10.5120/14883-3317
Abstract
This paper is attributed to develop a methodology for assisting the radiologists in proper identification of the diseases. In this paper we have considered the brain related disease archive from University of Rennes1 database to assist in finding the similar relevance images to the radiologists thereby helping them in drawing conclusions in their clinical practices. The methodology is implemented by considering the Generalized Gamma Distribution wherein the features extracted from the images using Discrete Cosine Transformation. Kullback–Leibler (KL) divergence methodology is used for finding the most similar images. The query image is processed and relevant images are retrieved. The final estimates of the parameters of Generalized Gamma Distribution are derived and the retrieved accuracy is identified using metrics.
References
- Nezamabadi-Pour, H. and Saryazdi, S. 2005. Object based image indexing and retrieval in DCT domain using clustering techniques. In Proceedings of World Academy of Science Engineering and Technology.
- Veltkamp, R. C. and Tanase, M. 2002. Content-based image retrieval systems: A survey. Department of Computing Science, Utrecht University, 1-62.
- Wang, J. andJi, L. 2001. A region and data hiding based error concealment scheme for images. IEEE Transactions on Consumer Electronics, 47(2), 257-262.
- Bajaj, M. and Lay, J. A. 2007. Image Indexing and Retrieval in Compressed Domain Using Color Clusters. IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007. pp. 271-274.
- Smeulders, A. W. , Worring, M. , Santini, S. , Gupta, A. and Jain, R. 2000. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1349-1380.
- Al-Jubouri, H. , Du, H. and Sellahewa, H. 2012. Applying Gaussian mixture model on Discrete Cosine features for image segmentation and classification. 4thIEEEConferenceon Computer Science and Electronic Engineering (CEEC), pp. 194-199.
- Al-Jubouri, H. , Du, H. and Sellahewa, H. 2013. Adaptive clustering based segmentation for image classification. 5th IEEE Conference on Computer Science and Electronic Engineering (CEEC) pp. 128-133.
- deVes, E. , Benavent, X. , Ruedin, A. , Acevedo, D. and Seijas, L. 2010. Wavelet-based Texture Retrieval Modeling the Magnitudes of Wavelet Detail Coefficients with a Generalized Gamma Distribution. 20thIEEEInternational Conference on Pattern Recognition (ICPR), pp. 221-224.
- Kai-Sheng, S. 2008. Globally Convergent Algorithms for Estimating Generalized Gamma Distributions in Fast Signal and Image Processing, IEEE Transactions on Image Processing, 17 (8) 1233-1250.
- Goldberger, J. , Gordon, S. and Greenspan, H. 2003. An efficient image similarity measure based on approximations of KL-divergence between two gaussian mixtures. InProceedings of theNinth IEEE International Conference on Computer Vision,vol. 1, pp. 487-493.
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