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

Rencan Nie,Min He,Zifei Liang,Jinde Cao,Dongming Zhou

doi:10.1007/s12652-018-1098-3

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.

Highlights

With the technological advancements, medical devices are routinely used to detect and record physiological signals that are essential for human health monitoring
EN is to measure the amount of information in enhanced image; the contrast improvement index (CII) reflects the degree of the contrast improvement for enhanced image compared with original image; structural similarity (SSIM) describe the structure similarity between original image and enhanced image; and edge preserve index (EPI) presents how well the salient edges in original image are preserved in enhanced image
From the aspect of objective evaluation, our method presents the best performance in terms of CII, SSIM and EPI for each Magnetic resonance imaging (MRI)-T2 image; contrast-limited adaptive histogram equalization (CLAHE) and homomorphic filtering (HF) show the worst scores in all metrics except MI

Summary

Introduction

Medical devices are routinely used to detect and record physiological signals that are essential for human health monitoring. Due to some factors such as environmental noises (Jeon 2017), light condition, constrained imaging techniques, and patients’ special conditions (Yang et al 2010; Hassanpour et al 2015), low resolution and contrast always are present in medical images, such that many important structures are not visible properly. In such case, it is difficult to segment or detect the boundaries of the abnormal structures/lesions or blood vessels presented in these poor images. Low resolution could be improved by some super-resolution technologies (Yang et al 2018; Wei et al 2017), and yet enhancing low contrast should be performed by image enhancement methods (Iqbal et al 2014; Tao et al 2018)

Methods

Results

Conclusion