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Computational imaging plays an important role in medical treatment for providing more comprehensive medical images. This work proposes a new scheme to fuse computed tomography (CT), magnetic resonance (MRI), and positron emission tomography (PET) images into a single image. A novel two-stage medical image fusion scheme, which is based on non-subsampled shearlet transform (NSST) and simplified pulse coupled neural networks (S-PCNNs), is proposed in the hue-saturation-value (HSV) color space. Firstly, CT and MRI images are decomposed into a set of low and high frequency coefficients by NSST, PET images are transformed into the HSV color space, and then the V component of PET image in the HSV color space. Secondly, intersecting cortical models (ICMs) are utilized to extract the edges and outlines in a larger area from the high frequency coefficients, and S-PCNNs are employed to …