Abstract
A novel image fusion technique is proposed in this paper to achieve an efficient and informative image by combining multiple medical images into one. This method accomplishes quaternion wavelet transform (QWT) as a feature representation technique and correlation metric for feature selection. Here, the correlation-based feature selection is accomplished to extract the optimal feature set from the sub-bands thereby to reduce the computational time taken for fusion process. QWT decomposes the source images first and then the feature selection process obtains an optimal feature set from the low-frequency (LF) as well as high-frequency (HF) sub-bands. The obtained optimal feature sets of both LF sub-bands and HF sub-bands are fused through low-frequency fusion rule and high-frequency fusion rule and the fused LF and HF coefficients are processed through IQWT to obtain a fused image. Various models of medical image are processed in the simulation and the performance is evaluated through various performance metrics and compared with conventional approaches to show the robustness and efficiency of proposed fusion framework.
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Krishna Chaithanya, J., Kumar, G.A.E.S., Ramasri, T. (2019). Correlative Feature Selection for Multimodal Medical Image Fusion Through QWT. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_133
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