Wavelet de-noising based microwave imaging for brain cancer detection

Haoyu Zhang, T. Arslan, B. Flynn
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引用次数: 6

Abstract

In microwave imaging for brain cancer detection, signals are generally degraded by noise. In this paper, we investigate the use of Discrete Wavelet Transform (DWT) based signal processing to improve the noise performance of an UWB based microwave imaging system for brain cancer detection. To test the noise suppression properties of the DWT, firstly, Gaussian white noise is added to the received pulse in a simulated microwave imaging system, such that the signal-to-noise ratios (SNRs) are 60dB and 45dB, respectively. These noisy signals are then processed and de-noised using the DWT. The de-noised signals are used to create cross-sectional images of a cancerous brain model, with the tumour highlighted. These resulting images demonstrate the validity of a DWT based de-noising method for brain cancer detection.
基于小波去噪的微波成像脑癌检测
在用于脑癌检测的微波成像中,信号通常会受到噪声的影响。在本文中,我们研究了使用离散小波变换(DWT)的信号处理来改善基于超宽带的脑癌检测微波成像系统的噪声性能。为了测试DWT的噪声抑制性能,首先,在模拟微波成像系统中,在接收到的脉冲中加入高斯白噪声,使其信噪比分别为60dB和45dB。然后使用DWT对这些噪声信号进行处理和去噪。去噪信号被用来创建癌变大脑模型的横截面图像,突出显示肿瘤。这些结果图像证明了基于DWT的去噪方法对脑癌检测的有效性。
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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