Combining Entropy Optimization and Sobel Operator for Medical Image Fusion

Fusing medical images is a topic of interest in processing medical images. This is achieved to through fusing information from multimodality images for the purpose of increasing the clinical diagnosis accuracy. This fusion aims to improve the image quality and preserve the specific features. The methods of medical image fusion generally use knowledge in many different fields such as clinical medicine, computer vision, digital imaging, machine learning, pattern recognition to fuse different medical images. There are two main approaches in fusing image, including spatial domain approach and transform domain approachs. This paper proposes a new algorithm to fusion multimodal images. This algorithm is based on Entropy optimization and the Sobel operator. Wavelet transform is used to split the input images into components over the low and high frequency domains. Then, two fusion rules are used for obtaining the fusing images. The first rule, based on the Sobel operator, is used for high frequency components. The second rule, based on Entropy optimization by using Particle Swarm Optimization (PSO) algorithm, is used for low frequency components. Proposed algorithm is implemented on the images related to central nervous system diseases. The experimental results of the paper show that the proposed algorithm is better than some recent methods in term of brightness level, the contrast, the entropy, the gradient and visual information fidelity for fusion (VIFF), Feature Mutual Information (FMI) indices.