Multi Modal Medical Image Fusion Based on Two Scale Image Decomposition and Spare Representation

Abstract:

Multimodality image fusion is a popular subject in the medical imaging industry because it improves clinical diagnostic accuracy by combining complimentary information from several pictures. This study introduces a multimodal picture fusion technique based on two-scale image decomposition and sparse representation. These processes include the Convolution Neural Network (CNN), to develop the network for images. With the help of network, feature maps can be developed for images. Finally, by using an enhanced decision maps and fusion scheme the fused image is obtained. The experimental results show that the proposed multimodal image fusion scheme outperforms with some others methods by performing qualitative and quantitative analysis.

Keywords: Convolution Neural Network (CNN), Feature Maps, Fusion.