Abstract
Recent research has reported the application of image fusion technologies in medical images in a wide range of aspects, such as in the diagnosis of brain diseases, the detection of glioma and the diagnosis of Alzheimer’s disease. In our study, a new fusion method based on the combination of the shuffled frog leaping algorithm (SFLA) and the pulse coupled neural network (PCNN) is proposed for the fusion of SPECT and CT images to improve the quality of fused brain images. First, the intensity-hue-saturation (IHS) of a SPECT and CT image are decomposed using a non-subsampled contourlet transform (NSCT) independently, where both low-frequency and high-frequency images, using NSCT, are obtained. We then used the combined SFLA and PCNN to fuse the high-frequency sub-band images and low-frequency images. The SFLA is considered to optimize the PCNN network parameters. Finally, the fused image was produced from the reversed NSCT and reversed IHS transforms. We evaluated our algorithms against standard deviation (SD), mean gradient (Ḡ), spatial frequency (SF) and information entropy (E) using three different sets of brain images. The experimental results demonstrated the superior performance of the proposed fusion method to enhance both precision and spatial resolution significantly.
Highlights
In 1895 Rontgen obtained the first human medical image by X-ray, after which research of medical images gained momentum, laying the foundation for medical image fusion
In order to verify the accuracy and preservation of the edge details in our proposed method, three sets of Computed Tomography (CT) and Single-Photon Emission ComputedBrain Medical Image FusionTomography (SPECT) images were fused based on our method
The results of each set were compared with four fusion methods; IHS, non-subsampled contourlet transform (NSCT)+FL, DWT, NSCT+pulse coupled neural network (PCNN)
Summary
In 1895 Rontgen obtained the first human medical image by X-ray, after which research of medical images gained momentum, laying the foundation for medical image fusion. It is necessary to fuse different modes of medical images into more informative images based on fusion algorithms, in order to provide doctors with more reliable information during clinical diagnosis (Kavitha and Chellamuthu, 2014; Zeng et al, 2014). Component substitution mainly refers to intensityhue-saturation (IHS) transform, with the advantage of improving the spatial resolution of SPECT images (Huang, 1999; Rahmani et al, 2010). Non-subsampled contourlet transform (NSCT) was proposed to fully extract the directional information of SPECT images and CT images to be fused, providing better performance in image decomposition (Da et al, 2006; Wang and Zhou, 2010; Yang et al, 2016)
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