This paper presents a curvelet based approach for the fusion of magnetic resonance (MR) and computed tomography (CT) images. The objective of the fusion of an MR image and a CT image of the same organ is to obtain a single image containing as much information as possible about that organ for diagnosis. Some attempts have been proposed for the fusion of MR and CT images using the wavelet transform. Since medical images have several objects and curved shapes, it is expected that the curvelet transform would be better in their fusion. The simulation results show the superiority of the curvelet transform to the wavelet transform in the fusion of MR and CT images from both the visual quality and the peak signal to noise ratio (PSNR) points of view.

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We present a new approach, based on the curvelet transform, for the fusion of magnetic resonance and computed tomography images. The objective of this fusion process is to obtain images, with as much detail as possible, for medical diagnosis. This approach is based on the application of the additive wavelet transform on both images and the segmentation of their detail planes into small overlapping tiles. The ridgelet transform is then applied on each of these tiles, and the fusion process is performed on the ridgelet transforms of the tiles. To maximize the benefit of the fused images, inverse interpolation techniques are used to obtain high resolution images from the low resolution fused images. Three inverse interpolation techniques are presented and compared. Simulation results show the superiority of the proposed curvelet fusion approach to the traditional discrete wavelet transform fusion technique. Results also reveal that inverse interpolation techniques have succeeded in obtaining high resolution images from the fused images with better quality than that of the traditional cubic spline interpolation technique.

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