基于萤火虫算法的混合滤波医学图像去噪

B. Sam, A. Lenin Fred
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引用次数: 3

摘要

近年来,图像处理在许多领域中扮演着不可或缺的角色,特别是在医学领域,我们需要精确的图像细节来进一步确定,诊断,研究目的等。医学图像,例如,CT, x光,核磁共振成像有一个缺陷,它们产生的图像带有噪声。这些噪音的产生是由于相机传感器单元,光线不好,传输等。这些混合的噪声应该从图像中去除,记住最终的目标是提供高质量的图像。因此,这里我们使用混合过滤器来评估图像中的噪声。然后利用人工神经网络系统对过滤后的输出图像进行萤火虫算法评估,以获得准确的图像细节。图像的性质是通过RMSE、PSNR和SSIM等策略来估计的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Denoising Medical Images Using Hybrid Filter With Firefly Algorithm
In recent days, image processing assumes an indispensable part in numerous fields, especially in medical fields where we require a precise image details for further determination, diagnosis, research purposes etc. Medical images, for example, CT, X-ray, MRI has an imperfection that they create images with noise. These noise emerge because of camera sensor cells, poor enlightenment, transmission etc. These blended sort of noise ought to be expelled from the images keeping in mind the end goal is to deliver a quality image. So here we utilize a blend of filters to assess the noise from the images. The filtered output images are then assessed by method for firefly algorithm in light of artificial neural network systems for the exact image details. The nature of the picture is estimated by strategy for RMSE, PSNR and SSIM and so forth...
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