基于Isomap算法的超声心动图降噪研究

P. Gifani, H. Behnam, Ahmad Shalbaf, Z. Sani
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引用次数: 4

摘要

在过去的二十年里,超声成像的医学应用得到了极大的扩展。在超声心动图中,为了更好地对大量数据集进行医学解释和诊断,降噪是一个具有挑战性的问题。本文将流形学习算法应用于二维超声心动图图像,以发现心脏运动连续周期帧之间的关系。该方法利用Isomap算法在重构的二维流形上用一个点来描述图像,并根据心跳周期的性质将相似图像相关的相似点放在一起。通过对重构流形上的相似图像进行平均来实现降噪。通过与一些常用方法的比较,并根据定性专家的意见,该方法在同类方法中降噪最大,模糊最小,对比度更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Noise reduction of echocardiography images using Isomap algorithm
Medical applications of ultrasound imaging have expanded enormously over the last two decades. De-noising is challenging issues for better medical interpretation and diagnosis on high volume of data sets in echocardiography. In this paper, manifold learning algorithm is applied on 2-D echocardiography images to discover the relationship between the frames of consecutive cycles of the heart motion. By this approach, each image is depicted by a point on reconstructed two-dimensional manifold by Isomap algorithm and similar points related to similar images according to the property of periodic heartbeat cycle stand together. Noise reduction is achieved by averaging similar images on reconstructed manifold. By comparing the proposed method with some common methods and according to qualitative expert's opinions, the proposed method has maximum noise reduction, minimum blurring and better contrast among the similar methods.
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