In the segmentation of brain image,the image quality is often reduced due to the influence of noise or outliers. And traditional fuzzy clustering has some limitations and is easily affected by the initial value,which brings great trouble for doctors to accurately identify and extract brain tissue. Aiming at these problems,an improved fuzzy clustering image segmentation method based on neighborhoods of image pixels constructed by Markov model was proposed. Firstly,the initial clustering center was determined by Genetic Algorithm(AG). Secondly,the expression of the target function was changed,the calculation method of the membership matrix was changed by adding the correction term in the target function and was adjusted by the constraint coefficient. Finally,the Markov Random Field(WRF)was used to represent the label information of the neighborhood pixels,and the maximized conditional probability of Markov random field was used to represent the neighborhood of the pixel,which improves the noise immunity. Experimental results show that the proposed method has good noise immunity,it can reduce the false segmentation rate and has high segmentation accuracy when used to segment brain images. The average accuracy of the segmented image has Jaccard Similarity(JS)index of 82. 76%,Dice index of 90. 45%,and Sensitivity index of 90. 19%. At the same time,the segmentation of brain image boundaries is clearer and the segmented image is closer to the standard segmentation image.

To deal with the mismatching between elevation and azimuth angles in orientation angle estimation of coherence signals by 2-D Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT)-like algorithm designed for the two-dimensional cross-shaped MEMS (Micro-Electro-Mechanical System) ultrasonic phased array, the improved 2-D ESPRIT-like algorithm based on the joint diagonalization of receive signal matrices was proposed. Firstly, the correlation matrices along x and y axis were derived by the received signal matrices to reconstruct the Toeplitz matrices to decoherence correspondingly according to ESPRIT-like algorithm. Secondly, the Toeplitz matrices were decomposed equivalently to obtain the equivalent received signal matrices after decoherencing. Finally, the joint diagonalization was used to diagonalize the equivalent received signal matrices to realize the matching between elevation and azimuth angles and estimate the orientation angles correctly. The simulation results show that the improved algorithm can estimate the orientation angles correctly compared with the algorithm before being improved. In comparison with the commonly used 2-D MUltiple SIgnal Classification (MUSIC) algorithm based on spatial smoothing, the response time of the proposed algorithm is decreased by 79%, the resolution of elevation and azimuth angles is increased by about 20% and 40% respectively, and the angle error is about 10% that of MUSIC algorithm when the SNR is 30 dB.

In order to overcome the oscillation caused by hard threshold wavelet filtering and the waveform distortion brought by soft threshold wavelet filtering, a wavelet threshold de-noising method based on genetic optimization function curve named GOCWT was proposed. In the GOCWT, a quadratic function was used to simulate the optimal threshold function curve. The Root Mean Square Error (RMSE) and smoothness of the reconstructed signal were applied to design the fitness function. Furthermore, the Genetic Algorithm (GA) was utilized to optimize the parameters of the new thresholding function. Through the analysis of 48 segments of ECG signals, it was found that the new method resulted in a 36% increase of smoothness value comparing to the hard threshold method, and a 32% decrease of RMSE value comparing to the soft threshold method. The results show that the proposed algorithm outperforms hard threshold wavelet filtering and soft threshold wavelet filtering, it can not only avoid the undesirable oscillation phenomenon of the filtered signal, but also reserve the minute features of the signal including peak value.

In order to study meridian characteristics, a feature extraction method of ElectroCardioGraph (ECG) signal on the meridian based on wavelet packet analysis and energy entropy was proposed. A meridian measuring experiment was firstly built to complete the acquisition of meridian data. Then meridian ECG signals were decomposed by a three layer wavelet packet decomposition. Energy entropy features of meridian ECG signals were extracted according to the results of signal reconstruction. After that, both K-means and Fuzzy C-Means (FCM) clustering techniques realized the effective partition of acupoints and non-acupoints. The derived clustering results indicate that the energy entropy values of ECG signals on the acupoints are obviously higher than those on the non-meridian points. It can be used as a powerful scientific basis for the discrimination of acupoints and non-acupoints.