Multimodal medical image fusion using residual network 50 in non subsampled contourlet transform

ABSTRACT

Medical image fusion technology and its collective diagnosis are becoming crucial day by day. This task confers the latest algorithm for image fusion of medical images to many diagnostic complications. Firstly, transform is employed on input source images. The result of the application of transform is the decomposition of source images into various subbands. Eminent features are extracted from these subbands by using resnet50. These features are fused by phase congruency and guided filtering fusion rules. Finally, inverse transform gives the original image. The experiment results of this algorithm are compared with different methods by taking some pairs of medical images. Subjective and objective outcomes prove that the proposed algorithm exceeds the current methods by giving optimal performance measures in the area of medical diagnosis. Thus, it is revealed that the suggested multimodal image fusion model provides elevated performance over existing models via diverse diseases using MRI-SPECT and MRI-PET.

KEYWORDS:

• Image fusion
• residual network50
• phase congruency
• guided filter
• non-sub-sampled contourlet transform
• non-subsampled
• directional filter bank
• medical modalities
• low-frequency and high-frequency subbands

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

K. Koteswara Rao

K. Koteswara Rao is a Research Scholar at the ECE Department of JNTUK Kakinada, Andhra Pradesh, India.

K. Veera Swamy

K. Veera Swamy is a Professor at the ECE Department of Vasavi College of Engineering, Hyderabad, Telangana, India.