Accurate estimation of ultrasound Doppler spectrogram envelope is essential for clinical pathological diagnosis of various cardiovascular diseases. However, due to intrinsic spectral broadening in the power spectrum and speckle noise existing in ultrasound images, it is difficult to obtain the accurate maximum velocity. Each of the standard existing methods has their own limitations and does not work well in complicated recordings. This paper proposes a robust automatic spectral envelope estimation method that is more accurate in phantom recordings and various in-vivo recordings than the currently used methods. Comparisons were performed on phantom recordings of the carotid artery with varying noise and additional in-vivo recordings. The accuracy of the proposed method was on average 8% greater than the existing methods. The experimental results demonstrate the wide applicability under different blood conditions and the robustness of the proposed algorithm.

Pulsed Wave Doppler (PW) is a traditional ultrasound technique used for the diagnosis of vascular diseases. The conventional diagnostic method is mainly based on hemodynamic parameters obtained from the PW spectrum. However, it relies on clinical observation and medical experience through lots of patient data investigation and analysis. The collected patient data are varied by different ultrasound equipment, detection regions and operation techniques, resulting in different image styles, which decreases the application and generality of the conventional method. And this method also has a strong dependence on patients’ data, especially on negative samples. Thus this paper proposes a rapid disease screening method, named as PauTa Criterion, which is based on statistical distribution characteristics for screening out anomalous targets. The proposed rapid screening method is based on multiple hemodynamic parameters to detect the outliers that are different from healthy samples. Compared with the conventional methods, the proposed method does not rely on a fixed or single ultrasound system and has low sensitivity to system noise. The experimental results show that the proposed method reaches a high accuracy of 93.14%, which is at least 20% higher than existing clustering methods, K-Means and Support Vector Machine (SVM). Accordingly, high accuracy and fast convergence makes the prospect of the proposed method to be used for rapid disease screening possible.

Pulsed Wave Doppler (PWD) is a traditional ultrasound technique used for the diagnosis of cardiovascular disease. The conventional diagnostic method is based on hemodynamic parameters obtained from the PW spectrum. However, it relies on the clinical experience of sonographers, and especially focusing on severe carotid stenosis. This paper proposes a classification method for the hemodynamic parameter using the RUSBoost algorithm. The proposed method improves the performance of RUSBoost by setting the empirical weight of each sample. The experimental results show that the proposed method reaches the accuracy of 90.1%, the sensitivity of 70%, and the specificity of 94%, which are 4%, 6%, and 2% higher than the original RUSBoost respectively. In addition, the proposed method is objective, since the empirical weights are computed based on Mahalanobis distance without any expert input. It can be used for the early detection of cardiovascular disease.