CT and MRI Image Fusion Based On 2-D Hilbert Transform

Image fusion algorithms plays a very important role in field of medical imaging applications like treatment planning and analyzing of multi-modality images. Generally the meaning of image fusion is the process of combining the complementary information from different sources into a single image. The main goal of medical image fusion is to associate the information from multiple medical images of many medical imaging modalities like Computed Tomography (CT), Magnetic Resonance Image (MRI) to provide more information for doctors and clinical treatment planning and medical diagnosis. In this paper a new image fusion method is proposed based on two dimensional Hilbert transform (2-D HT) to fuse CT and MRI images. This transform used in various fields of image processing but up to now this transform was not use in image fusion, especially medical image fusion field. The main purpose of this paper is verifying the desirable performance of two dimensional Hilbert transform (2-D HT) in CT and MRI image fusion. Analysis of qualitative and quantitative fusion metrics such as Root Mean Score Error (RMSE) and Peak to Signal Noise Ratio (PSNR) that we used in this paper, obviously proves that our proposed method based on using maximum rule 2- D HT_Max exhibits greater results than other well fusion rules that we used for combining the two dimensional Hilbert transform (2-D HT) coefficients.

CT and MRI Image Fusion Based On 2-D Hilbert Transform Keywords:

Image fusion , Medical Image Fusion (MRI) , two dimensional Hilbert transform (2-D HT) , Fusion rules , Root Mean Score Error (RMSE) , Peak to Signal Noise Ratio (PSNR)