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

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.

Altered Patterns of Functional Connectivity and Causal Connectivity in Salience Subnetwork of Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment

“…Alzheimer disease (AD) is a chronic neurodegenerative disorder presented in elderly individuals with conspicuous decline in cognitive deterioration and lapse of memory (Huang et al, 2019;Wessels et al, 2019;Zhang et al, 2019). As one of the phases between normal aging and dementia, amnestic mild cognitive impairment (aMCI) subjects have a 10-15% possibility of developing into AD per year (Petersen et al, 2002;Yang et al, 2017).…”

A Review of Multimodal Medical Image Fusion Techniques

“…The model is used to fuse high-and low-frequency coefficients, and the fused image has better robustness, reliability, and visual effects. Recent research has emerged a fusion algorithm combining NSCTbased PCNN and shuffled frog leaping algorithm, which significantly improves the spatial resolution [19]. Shabanzade and Ghassemian [15] proposed that in the multimodal fusion method based on NSCT, the sparse representation algorithm is used to fuse the low-frequency bands, and how to select a good dictionary is the key to the sparse representation algorithm.…”

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

“…Nevertheless, PCNN has its shortcomings, such as numerous parameters and the complex process of setting parameters. To date, several novel algorithms to optimize the PCNN parameters have been introduced to improve calculation speed [91,92]. In image fusion, PCNN has also been used as fusion rule for wavelet transform-based methods, such as DWT [93][94][95][96], Contourlet transform [97][98][99][100], NSCT [101][102][103][104][105], ST [106][107][108][109] and NSST [110].…”