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Fusion of Multi-slice MR-Scan Images with Genetic Algorithm with Curvelet-transform | OMICS International | Abstract
ISSN: 2090-9888

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Research Article

Fusion of Multi-slice MR-Scan Images with Genetic Algorithm with Curvelet-transform

Dhanesh Kumar Solanki1*, Naresh Solanki2 and Mohammed Fakhreldin Adam2

1Department of Computer Science Engineering, Birla Institute of Technology PILANI, Kota Medical College, Rajasthan, India

2Assistant professor, University Malaysia Pahang, Malaysia

*Corresponding Author:
Dhanesh Kumar Solanki
Department of Computer Science Engineering
Birla Institute of Technology PILANI
Kota Medical College, Rajasthan, India
Tel: +91-9414062512
E-mail: [email protected]

Received date: September 03, 2014; Accepted date: October 24, 2014; Published date: October 31, 2014

Citation: Solanki DK, Solanki N, Adam MF (2014) Fusion of Multi-slice MR-Scan Images with Genetic Algorithm with Curvelet-transform. Lovotics 2:109. doi:10.4172/2090-9888.1000109

Copyright: © 2014 Solanki DK, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

On considerable progress on Medical Image fusion techniques, many algorithm are used from intensity-huesaturation (IHS), principal component analysis (PCA), multi resolution analysis based method & Artificial Neural Network (ANN), but all these algorithm had heavily degrade the brightness of the input images. In IHS fusion, it converts a low resolution color images from RGB space into the IHS color space and a combined IHS with PCA to improve fused image quality but it generates several drawback, (a) The Pixel-level fusion- method are sensitive to accuracy (gives low accuracy), (b) The color channel of input spectrum should be less than or equal to IHS transform (created very large spectrum) and in ANN-based fusion include: (1) The long iteration time of the ANN and the need to manually parameterize it before each fusion; (2) An occasional failure to converge, such as in the PCNN-based method when some neurons did not fire during the whole iteration.

Keywords

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