Improving the quality of medical images in shearlet domain

N. Binh, V. T. H. Tuyet
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引用次数: 4

Abstract

In clinical diagnostics, the diagnostic approach based on images obtained from equipment of which medical machine (diagnostic imaging) plays an important role. However, most of medical images have blur combined with noise. There are many reasons to create blur combined with noise in medical images such as the environment, capture device, technician's skills, etc. This problem will affect the process diagnose. In this paper, we proposed a new method to improve the quality of medical images. The proposed method uses cycle spinning combined with Kernels set in shearlet domain. Our algorithm removes the noise and blur details in shearlet domain, and must not the value of point-spread function (PSF). The proposed algorithm not only significantly improves the edge accuracy, but also reduces the loss of information in medical image.
提高剪切波域医学图像的质量
在临床诊断中,基于医疗设备图像的诊断方法(诊断成像)起着重要的作用。然而,大多数医学图像都存在模糊和噪声。在医学图像中产生模糊和噪声的原因有很多,比如环境、捕捉设备、技术人员的技能等。这个问题会影响过程诊断。本文提出了一种提高医学图像质量的新方法。该方法采用循环自旋和剪切波域核集相结合的方法。该算法消除了剪切波域中的噪声,模糊了细节,并且不影响点扩散函数(PSF)的值。该算法不仅显著提高了边缘精度,而且减少了医学图像中的信息丢失。
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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