Dissertação de mest., Engenharia Electrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2011The pulsed Doppler ultrasound (DU) is one of the important tools in the study
of vessel diseases and the investigation of flow conditions. Due to its non-invasive
nature, it has been increasingly used in medicine in the last few decades. Accurate
estimation of DU spectral center frequency and bandwidth parameters are
extremely important for blood flow diagnostic purposes. Under real-time data acquisition
conditions the DU signal is generally corrupted with different types of
noise. In these situations the identification of signal components solely belonging
to the blood flow signal is a difficult task.
This thesis was aimed to study spectral techniques to enhance spectral parameter
estimation, in particular the center frequency. Spectral estimates were obtained
using the Short Time Fourier Transform (STFT) and Continuous Wavelet
Transform (CWT). STFT was applied to short duration data segments, respecting
signals’ stationary properties. Two CWT functions have been studied: varying
bandwidth filter and fixed bandwidth filter. Since different filter bandwidth values
yield different results, bandwidths for fixed bandwidth filter were investigate and
the most proper one has been used on the performance comparative studies. To
enhance the blood flow signal content of noise-embedded clinical Doppler signals,
a STFT-based technique was proposed to reduce the signals’ noise components.
Quantitative evaluation of the spectral methods was primarily performed on
simulated signals with deterministic center frequency and bandwidth. Different
signal to noise ratio signals were simulated. It has been observed that STFT spectral
center frequency and bandwidth estimators were less biased than the CWT
ones, although the last ones were less sensitive to the center frequency variations.
Applying the proposed noise cancellation technique to simulated signals reduces
the spectral estimators’ errors. As an example, a typical noisy signal with
10dbSNR, a reduction of 88% and 97% was obtained on the RMS bias of the
estimation of the center frequency and bandwidth estimators respectively
