De-Noising of Uterus Fibroid Ultrasound Image Using Gaussismooth Convolution Filter (GSCF)

M. Devi, V. Sindhu
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Abstract

This paper discusses the methods to detect the presence of uterus fibroid in woman by implementing various image processing techniques. The input image is an ultrasound image as it is cost effective when compared to other imaging techniques like CT, MRI. The initial step in image processing is to remove noise by applying filters. Application of filters smoothen the image without blurring the image. Gradient of the processed image is calculated and the image is enhanced by sharpening the edges of the image are achieved by calculating the local maxima of the gradient. Then, the edges are decided by calculating the threshold value of the processed image. The proposed Gaussismooth Convolution Filter gives better results when compared with other existing filter with PSNR value of 94%.
基于高斯平滑卷积滤波器的子宫肌瘤超声图像去噪
本文讨论了利用各种图像处理技术检测女性子宫肌瘤的方法。输入图像是超声图像,因为与其他成像技术(如CT, MRI)相比,它具有成本效益。图像处理的第一步是用滤波器去除噪声。滤镜的应用使图像平滑而不使图像模糊。计算处理后图像的梯度,并通过锐化图像来增强图像,通过计算梯度的局部最大值来实现图像的边缘增强。然后,通过计算处理后图像的阈值来确定边缘;本文提出的高斯平滑卷积滤波器与已有的滤波器相比,具有更好的滤波效果,PSNR可达94%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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