Pulse coupled neural network based MRI image enhancement using classical visual receptive field for smarter mobile healthcare

Abstract

With the rapid growth of medical big data, medical signal processing measurement techniques are facing severe challenges. Enormous medical images are constantly generated by various health monitoring and sensing devices, such as ultrasound, MRI machines. Hence, based on pulse coupled neural network (PCNN) and the classical visual receptive field (CVRF) with the difference of two Gaussians (DOG), a contrast enhancement of MRI image is suggested to improve the accuracy of clinical diagnosis for smarter mobile healthcare. As one premise, the parameters of DOG are estimated from the fundamentals of CVRF; then the PCNN parameters in image enhancement are estimated eventually with the help of DOG. As a result, the MRI images can be enhanced adaptively. Due to the exponential decay of the dynamic threshold and the pulses coupling among neurons, PCNN effectively enhances the contrast of low grey levels in MRI image. Moreover, because of the inhibitory effects from inhibitory region in CVRF, PCNN also effectively preserves the structures such as edges for enhanced results. Experiments on several MRI images show that the proposed method performs better than other methods by improving contrast and preserving structures well.

Evaluation criterion of underwater object clustering segmentation with pulse‐coupled neural network

“…Compared with DB, U i j are used for replacing pixel values to participate in DB-PCNN and improve the disadvantages of noise sensitivity and missing contour of the region. In DB-PCNN, (12) and (14) are redefined as: (21) where U represents the internal active item corresponding to x. U c i represents the internal activity item corresponding to the clustering centre c i .…”

“…Pulse-coupled neural network (PCNN) simulates the phenomenon of the synchronous pulse of mammalian visual cortex neurons [9]. Therefore, compared with general image processing methods, it has more reasonable operation mechanism and is widely used in many image processing areas, such as brain medical image fusion [10], SAR image feature extraction [11], and magnetic resonance imaging image enhancement [12]. Some methods such as adversarial domain adaptation [13] and contour awareness [14] are used to improve the deep neural network model and have achieved good results in image segmentation tasks, but a large quantity of data is needed to complete the training process.…”

Fuzzy-rough assisted refinement of image processing procedure for mammographic risk assessment

“…where, V E is the amplitude constant; α E is the time decay constant of the dynamic threshold E ij , determining the number of iterations in a cycle where all pixels are processed; and Y ij is the pulse output function of PCNN [46].…”

A Novel Medical Image Fusion Method Using Multi-Channel Pulse Coupled Neural Networks

“…With the gradual deepening of research on single-and dual-channel PCNN, the method is becoming one of the most popular method in the image fusion. Compared with the wavelet transform [1] that has been applied to multi-focus image fusion, FSD [2] and Gradient pyramid [3], PCNN is still a research focus of multi-focus image fusion, medical image fusion, and the well-known works are [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

“…Firstly, the relevant scholars put forward and summarized the background, principles, further development of the condition and application prospects of PCNN model [4][5][6][7][8][9][10], which laid the foundation for the further development of the model. Then, an adaptive dualchannel pulse-coupled neural network (PCNN) with triplelinking strength (ATD-PCNN) is proposed.…”