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

Chenxi Huang; Ganxun Tian; Yisha Lan; Yonghong Peng; E Y K Ng; Yongtao Hao; Yongqiang Cheng; Wenliang Che

doi:10.3389/fnins.2019.00210

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

Front Neurosci. 2019 Mar 20:13:210. doi: 10.3389/fnins.2019.00210. eCollection 2019.

Authors

Chenxi Huang¹, Ganxun Tian¹, Yisha Lan¹, Yonghong Peng², E Y K Ng³, Yongtao Hao¹, Yongqiang Cheng⁴, Wenliang Che⁵

Affiliations

¹ Department of Computer Science and Technology, Tongji University, Shanghai, China.
² Faculty of Computer Science, University of Sunderland, Sunderland, United Kingdom.
³ School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore.
⁴ School of Engineering and Computer Science, University of Hull, Kingston upon Hull, United Kingdom.
⁵ Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.

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.

Keywords: computed tomography image; image fusion; pulse coupled neural network; shuffled frog leaping; single-photon emission computed tomography image.