Wavelet transform is internationally recognized a good tool for time-frequency analysis. This paper presents a novel speckle suppression method for medical B-Scan images. The speckle is produced by scattering echoed signals in different phases because of wave interference. The software MATLAB is used as operating platform meanwhile the wavelet is used to eliminate the noise. The ultrasound image is processed by adaptive method and then the selected sym8 wavelet is used in the multiresolution analysis of the ultrasound image. The result shows that this method can effectively reduce the speckle noise.

This paper discusses the recognition of heart sound for evaluating the cardiac contractility change trend, which includes heart sound samples recorded at different exercise condition. Especially, focused on the recognition of heart sound recorded after high intensity exercise workload. The algorithm proposed consisted of two correlative methods. The first was to recognize heart sound recorded at rest and after low intensity exercise workloads by probabilistic neural network and the second was to recognize heart sound recorded after high intensity exercise workloads based on the characteristic of heart sound. Both methods have two consecutive phases. Firstly, all peaks, including the peaks of both heart sounds and noise, are marked by a repetitive threshold detecting algorithm. Secondly, probabilistic neural network is employed to classify the peaks detected in the first phase into Si, S2, and noise. Finally, the performance of the algorithm was evaluated using 45 digital heart sound recordings including normal and abnormal heart sound, which were recorded at rest and after low intensity exercise workloads, and 28 digital heart sound recordings recorded after high intensity exercise workloads. The results showed that over 94% of heart sound samples were classified and recognized correctly. Moreover, the reasons for the wrong classification, of which omitting and misdetection are two main problems, are also discussed and solutions are proposed. So this method can be improved and refined in following studies. In conclusion, this algorithm is a reliable approach to detect and classify heart sounds, providing a solid basis for further heart sound analysis.
Algorithms ; Heart Sounds ; physiology ; Humans ; Neural Networks (Computer) ; Phonocardiography ; Signal Processing, Computer-Assisted