A New Pulse Coupled Neural Network (PCNN) for Brain Medical Image Fusion Empowered by Shuffled Frog Leaping Algorithm

• Published in: Frontiers in neuroscience Vol. 13; p. 210
• Main Authors: Huang, Chenxi, Tian, Ganxun, Lan, Yisha, Peng, Yonghong, Ng, E. Y. K., Hao, Yongtao, Cheng, Yongqiang, Che, Wenliang
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 20.03.2019; Frontiers Media S.A
• Subjects: Algorithms; computed tomography image; Computer science; Diagnosis; Dictionaries; Disease; Entropy; Frequency dependence; Glioma; image fusion; Immunoassay; Kalman filters; Methods; Neural networks; Neuroimaging; Neuroscience; NMR; Nuclear magnetic resonance; Optimization; pulse coupled neural network; Remote sensing; shuffled frog leaping; Single photon emission computed tomography; single-photon emission computed tomography image; Wavelet transforms; United Kingdom > UK; China; Singapore; single-photon emission computed tomography image; computed tomography image; image fusion; pulse coupled neural network; shuffled frog leaping
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2019.00210

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.