Study of image fusion optimization techniques for medical applications

Abstract

Image fusion is becoming increasingly important in computer vision activities because of the larger number of capture methods. The integration of different views like multi view, multi temporal view and information which is larger together termed as image fusion (IF). In image fusion the information of multiple-sensors are converted into a unified image while maintaining the fidelity of critical characteristics.Healthcare image fusion procedures are used to improve picture quality by achieving the conspicuous characteristics in the fusion findings. As a result, they increase the practical usefulness of medical pictures for issue assessment and identification. Fusion of medical images are generally evaluated using the modalities like MRI-Magnetic Resonance Imaging, MRA-Magnetic Resonance Angiogram, PET-Positron Emission Tomography, SPET-Structural Positron Emission Tomography,CT-Computed Tomography, and SPECT-Single-Photon Emission Computed Tomography. Neural network and optimization techniques help in improving the quality of fused image. In this paper various fusion techniques are studied. Along with different fuson approaches, the outcomes of diverse research projects are contrasted in terms of how the pulse coupled neural network is employed and different optimization techniques. The Pulse Couple Neural Networks (PCNN) using various optimization techniques are compared. Among which the swarming mechanism of salps improves the performance of the system. The PCNN is combined with SSO algorithm and evaluated the results.From the results it is shown that PCNN- Salp Swarm Optimization (SSO)ishaving good value of Peak Signal to Noise Ratio (PSNR) with 45.93 and Structural Similarity Index(SSIM) is 0.996.

Keywords

• Imaging modalities
• Medical image fusion
• MRI
• CT
• PET
• SPECT