medical images fusion based on equilibrium optimization and discrete wavelet transform

Integrating multimodal medical imaging has many advantages for diagnosis and clinical analysis because it creates the conditions for physicians to make more accurate diagnoses. to the best of our knowledge, there are still some disadvantages to current image fusion methods. first, image fusion often has low contrast due to the law of weight average to combine low-frequency components. the second problem is the loss of accurate information in the merged image. this paper presents a wavelet-based method and equilibrium optimization for mri and pet medical image fusion to obtain a high-quality image fusion. in the proposed method, the equilibrium optimization algorithm finds the appropriate common points in mri and pet images and performs the combination with the help of wavelet transform. this allows the welded image to retain the details transferred from the mri images significantly. experimental results show that the proposed approach is effective in significantly increasing the quality of the integrated image and preserves the insignificant information transmitted from the input images.