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Brain-computer interface (BCI) provides a direct communicating and controlling approach between the brain and surrounding environment, which attracts a wide range of interest in the fields of brain science and artificial intelligence. It is a core to decode the electroencephalogram (EEG) feature in the BCI system. The decoding efficiency highly depends on the feature extraction and feature classification algorithms. In this paper, we first introduce the commonly-used EEG features in the BCI system. Then we introduce the basic classical algorithms and their advanced versions used in the BCI system. Finally, we present some new BCI algorithms proposed in recent years. We hope this paper can spark fresh thinking for the research and development of high-performance BCI system.

Colon cancer is one of the leading causes of cancer-related deaths, while the virtual colonoscopy plays an important role in the early detection of colon cancer. Effective colon segmentation is the basis of virtual colonoscopy. Oral contrast agent can reduce the use of laxatives, but it can to bring some difficulties in the colon segmentation. Because of the partial volume effect, the CT value of air-tagging boundary span a wide range, which are not easily eliminated. We present an effective method of colon segmentation in this paper. Firstly, we used adaptive threshold method to extract air area. Then we segmented tagging region and removed air-tagging boundary by segment region growing method based on statistical information and morphological operation. The experiments showed that the agreement between the results by manual and by orithm segmentation reached 93.4%.

At present, ray-casting algorithm is the most widely used algorithm in the field of medical image visualization, and it can achieve the best image quality. Due to large amounts of computation like sampling, gradient, lighting and blending calculation, the cost of ray-casting algorithm is very large. The characteristic of Streaming single instruction multiple datum extensions (SSE) instruction--supporting vector computation--can satisfy the property of ray-casting algorithm well. Therefore, in this paper, we improved the implementation efficient significantly by vectorization of gradient, lighting and blending calculation, and still achieved a high quality image at the same time.

Pattern selection plays an important role in data mining and pattern recognition, especially for large scale bioinformatic data. There are many problems in this field, such as algorithm complexity and numbers of the best feature subset. In this paper, we propose a new pattern selection algorithm, carrying out pattern selection base on Mutual Information (MI). Pattern subset evaluation index was studied to ensure the best feature subset. To pattern selection, algorithm bases on the correlation of patterns and label, as well as the redundancy of each pattern. Neurofuzzy Pattern Subset Evaluation Index was researched to make sure which is the best subset for our pattern subset evaluation. To verify the effectiveness of our method, several experiments are carried out on the data of gene expression of mouse from Leiden University and UCI datasets. The experimental results indicated that our algorithm achieved better results in the complexity and accuracy.

In this paper, an image mosaic algorithm based on feature points was proposed by making a study of the image mosaic methods and the characteristics of pathological image. Points of interest were firstly extracted by Moravec operator in this method, and the corresponding feature matches of the original images were achieved by correlation coefficient. The parameters of affine transformation were calculated by the matching feature points, and weighted average method was used to fuse images. The experimental results show that, as for the pathological image with translation and rotation, the algorithm can extract the matching feature points accurately and realize the image seamless mosaic.

Ultrasound based tissue thermal lesion non-invasive detection is of great significance in high intensity focused ultrasound (HIFU) clinical application. In this paper, we propose a sub-pixel method to quantify the ultrasound image change caused by HIFU as correlation-distance. The support vector machine (SVM) was trained by using correlation distance as samples, and the recognition effect was tested. Results showed that sub-pixel cross-correlation vector field could reflect the ablation lesions position. SVM based classification method can recognize HIFU beam lesion degree effectively.

In the field of computational molecule biology, there is still a challenging question of how to detect non-coding RNA gene in lots of unlabeled sequences. Generally, the methods of machine learning and classification are employed to answer this question. However, only a limited number of positive training samples and unlabeled samples are available. The negative samples are difficult to define appropriately, yet they are necessary for usual learning-then-classification method. The common way for most of the existing non-coding RNA gene finding methods is to produce a number of random sequences as negative samples, which may hold some characteristic of positive sample sequences. Consequently, the contrived uncertain factor was introduced and the performance of methods was not good enough. In this paper, Support Vector Data Description (SVDD) is in use for to learning and classification as well as for detecting non-coding RNA gene in lots of unlabeled sequences, and the k-means clustering algorithm is employed before SVDD training to deal with the high flase positive fault in the result of SVDD. The training samples (target samples) are non-coding RNA genes validated by experiment. Moreover, appropriate features were constructed by Principal Component Analysis (PCA). The effectiveness and performance of the method are demonstrated by testing the cases in NONCODE databases and E. coli genome.

In allusion to non-rigid registration of medical images, the paper gives a practical feature points matching algorithm--the image registration algorithm based on the scale-invariant features transform (Scale Invariant Feature Transform, SIFT). The algorithm makes use of the image features of translation, rotation and affine transformation invariance in scale space to extract the image feature points. Bidirectional matching algorithm is chosen to establish the matching relations between the images, so the accuracy of image registrations is improved. On this basis, affine transform is chosen to complement the non-rigid registration, and normalized mutual information measure and PSO optimization algorithm are also chosen to optimize the registration process. The experimental results show that the method can achieve better registration results than the method based on mutual information.

This paper addresses the predicting problem of peritoneal fluid absorption rate(PFAR). An innovative predicting model was developed, which employed the improved genetic algorithm embedded in neural network for predicting the important PFAR index in the peritoneal dialysis treatment process of renal failure. The significance of PFAR and the complexity of dialysis process were analyzed. The improved genetic algorithm was used for defining the initial weight and bias of neural network, and then the neural network was used for finding out the optimal predicting model of PFAR. This method utilizes the global search capability of genetic algorithm and the local search advantage of neural network completely. For the purpose of showing the validity of the model, the improved optimal predicting model is compared with the standard hybrid method of genetic algorithm and neural network. The simulation results show that the predicting accuracy of the improved optimal neural network is greatly improved and the learning process needs less time.

The cough sound is a very important symptom of well over 100 kinds of diseases. Cough sound analysis can provide much information which is useful for diagnosing. Detecting the frequencies and intensity of cough can evaluate the efficiency of therapy quantitatively. In this paper, we put forward an algorithm for cough sound recognition. We first decompose signals with wavelet transform and calculate the normalized energy at each time-frequency point. Then we obtain the normalized energy distribution statistically. After that, we pick out the time-frequency points maximizing a certain discriminant measure of normalized energy distribution between cough sound and non-cough sound, and then we use the normalized energy belonging to these time-frequency points as the inputs of Linear discriminant analysis/Generalized singular value decomposition (LDA/GSVD) classifier. The experimental results show that the classification accuracies achieved by using the algorithm is about 85%, and the computation complexity is low.

The chaos of heart rate variance (HRV) is identified by surrogate data method in this paper. Firstly, the main principle of chaotic identification is introduced, in which the median absolute error (MAE) of one-step prediction for HRV is set as the statistic. The algorithm is checked with a known chaotic system response and a colored noise signal. Then, some typical healthy and unhealthy HRVs are analyzed with the method of surrogate data, and some characteristic parameters from this method are compared.

It is very difficult to use the conventional methods that are based on area and on border for extracting a region of intered such as liver tumor region with vague and irregular boundaries. This paper introduces Poisson matting that uses transparency (alpha value) as self-property of image and seeks its best result value to achieve the aim of extracting object. Meanwhile, this method is applied to the liver tumor CT picture experimental results. It makes improvement in two steps: computing image source a value; calculating foreground image F and background image B. Consequently, successful result is obtained.

Image guided surgery (IGS) means that computers are used for modeling the position of patients and providing the surgeons with image guidance for invasive procedures. In this paper, we introduce the composition and the work flow of IGS, analyze the process and principle of Registration, which is regarded as a process to combine the actual position of patients and the visual position of models. Seeing that the precision of the registration determines the precision of the IGS system directly, we conduct a study on the registration algorithm precision, feasibility as well as robustness. Combined with the actual requirements in clinical IGS, we also analyze the influence of changes in the number and position of markers on registration precision.

To solve the problem of imprecise positioning of feature point and of the feature data redundancy in facial expression recognition by active appearance models (AAM), the automatic adjustment of initial model for AAM fitting is proposed in this paper. The specific aims are to improve the precision of positioning and to more effectively reflect the variation of expressions by acquired features. The problem of feature selection is resolved by adopting quadratic mutual information and reducing the feature dimension. The support vector machine (SVM) classifier is used for expression recognition. The experimental results on CAS-PEAL facial expression database show that the proposed method effectively improves the performance of facial expression recognition, the maximum recognition rate being 83.33%.

How to recognize and diagnose heart diseases, as one of the most frequent issues, is a serious problem. ECG automation recognition is not only the target of medicine, but also the work of computer science. In this paper, the approaches to ECG recognition are reviewed, and their characters are analyzed. With the background of wearable wireless ECG monitor, on the basis of experts' experience in ECG recognition, the imagery thinking-based new method, in which morphology parameters are considered, is presented.

In present, the most basically used parameters for speaker identification are linear predictive coding (LPC) parameter, Mel frequency cepstrum coefficient(MFCC), etc. First in this paper only MFCC was used as the parameter and then Lempel-Ziv Complexity was combined with MFCC as parameters. The text-dependent recognition rate of 50 speakers increased from 42% to 80% and the text-independent recognition rate of 50 speakers increased from 60% to 72%. This test shows that Lempel-Ziv complexity, as a new parameter, can be applied to speaker identification.

Some noises still exist in the single-trial averaged visual evoked potentials (VEP), so further extraction of the above results is of significance. Independent component analysis (ICA)can separate the sources from their mixtures and make the output statistically as independent as possible; it can remove noises effectively. In this paper, the principle, experiment analyses and results of ICA based on quasi-Newton iteration rule for VEP feature extraction are introduced, It is compared with the fixed-point FastICA algorithm. The experiment results show that the provided algorithm may reinforce signals effectively and extract distinct P300 from the single-trial averaged VEP. It is of good applicability.