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Optimal Deep CNN–Based Vectorial Variation Filter for Medical Image Denoising

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Abstract

Medical imaging has acquired more attention due to the emerging design of wireless technologies, the internet, and data storage. The reflection of these technologies has gained attraction in medicine and medical sciences facilitating the diagnosis and treatment of different diseases in an effective manner. However, medical images are vulnerable to noise, which can make the image unclear and perplex the identification. Thus, denoising of medical images is imperative for processing medical images. This paper devises a novel optimal deep convolution neural network–based vectorial variation (ODVV) filter for denoising medical computed tomography (CT) images and Lena images. Here, the input medical images are fed to a noisy pixel map identification module wherein the deep convolutional neural network (Deep CNN) is adapted for discovering noisy pixel maps. Here, Deep CNN training is done with the Adam algorithm. Once noisy pixels are identified, it is further given to noise removal module which is performed using the proposed optimization algorithm, namely Feedback Artificial Lion (FAL). Here, the FAL is devised by combining the FAT and Lion algorithm. After noise removal, the pixel enhancement is performed using the vectorial total variation norm to get final pixel-enhanced image. The proposed FAL algorithm offered enhanced performance in contrast to other techniques with the highest peak signal-to-noise ratio (PSNR) of 24.149 dB, highest second-derivative-like measure of enhancement (SDME) of 32.142 dB, highest structural index similarity (SSIM) of 0.800, and Edge Preserve Index (EPI) of 0.9267.

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Data Availability

Dataset is available on online web portal of NIH clinical centre and accessible to everyone.

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  1. Dept. of Biomedical Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Sonipat, Haryana, 131039, India

    Dinesh Kumar Atal

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Atal, D.K. Optimal Deep CNN–Based Vectorial Variation Filter for Medical Image Denoising. J Digit Imaging 36, 1216–1236 (2023). https://doi.org/10.1007/s10278-022-00768-8

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