Optimal bilateral filter and Convolutional Neural Network based denoising method of medical image measurements

Image denoising has been foremost concern in the field of medical imaging (MI). For image denoising, the most challenging is to protect the data bearing structures such as edges and surfaces to get good visual quality while enhancing Peak Signal to Noise Ratio (PSNR). In this work, an inventive bio-inspired optimization based filtering system is considered for the MI denoising process, the filter named as Bilateral Filter (BF). The execution of the denoising process influences the decision of selecting the optimal parameters, i.e., Gaussian and spatial weights. Here, these parameters are chosen by utilizing swarm based optimization that is Dragonfly (DF) and Modified Firefly (MFF) algorithm. For this parameter selection, multiobjective fitness function (PSNR and vector root mean square error (VRMSE)) is utilized. Moreover, Convolutional based Neural Network (CNN) classifier is used to classify the denoised image as normal or abnormal, with better classification rate. From the experimental results the proposed model, PSNR of 47.52 dB and error rate of 1.23 is attained contrasting to the existing filters and some classifiers. (C) 2019 Elsevier Ltd. All rights reserved.