Wavelet de-noising based microwave imaging for brain cancer detection

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.