Objective To register two breath-hold lung volumes image from one subject with deep expiration and deep inspiration.Methods Three pairs of thoracic high resolution CT serial from three subjects were collected under two breath-hold respiration stages.The lung parenchyma of every serial was segmented using the serial segmentation algorithm.Left and right lungs were stored separately.Expiration and inspiration volume images of single lung were registered.Firstly,affine transformation parameters were found based on the anatomic flag surfaces and expiration image volume was re-sampled with affine transformation.Secondly,"Demons" algorithm was employed to register two image volumes non-rigidly.Results Two lung surfaces and the inner structures have a nice registration.The average volume overlap of two images before registration is 0.7982.After global affine transformation,it improves to 0.8936.After "Demons",it is up to 0.9544.The average descending percentage of root mean square errors is 19.83%(after the global affine transformation) and 49.43%(after the "Demons" non-rigid registration).Conclusion The intra-subject registration between two lung image volumes with large deformations described here has an effective registration result.It offers a good base to analyze the lung respiration function.

Objective: To develop a way of high-quality real-time three dimension surface reconstruction for high-resolution volume data.Method 3D surface point sets of single organ were using a method of binding the threshold and morphological operations.The normal vector of every surface point was calculated.According to the gray gradients of volume data,the triangle face was replaced by surface points to describe the organ surface,and the surface was displayed with OpenGL interface of display card after defining the color and transparent of the organ surface.Result Based on hardware platform of personal computer,the reconstruction of skeleton and skin for the digitized virtual Chinese man No.1(VCH-M1) from CT database was constructed,the rendering speed was faster than 25 F/s.Conclusion The algorithm is capable of realizing a real-time rendering for 512?512?1720 high resolution volume data.

Objective To search an approach based on Support Vector Machine (SVM) for detection of different abnormalities including micro-calcifications and masses from digital mammograms. Methods Such detections were formulated as supervised-learning problems and SVM was applied to the detection algorithm. After the regions of interest were pre-processed by specific rectangular windows, three kinds of parameters were extracted, including the direct pixel value parameter, the parameters from Spatial Grey Level Dependency (SGLD) matrices and from Discrete Cosine Transform (DCT). At first, each kind of parameter was taken as the input of SVM to train and test the machine respectively. Then all the parameters were incorporated into the input of SVM. Results the classification accuracy is 92.28%, 90.35% and 91.12% respectively when only one parameter input. The classification accuracy reaches 99.23% when all the parameter incorporated. Conclusion The parameters extracted from the regions of interest in digital mammograms can reflect the characteristics of different regions and SVM is a powerful tool for the detection of abnormalities from digital mammograms.

In order to study event-related desynchronization (ERD) related to voluntary movement, we designed two experiments. In the first experiment, untrained subjects were required to imagine the action of typing with left or right index finger for about 1 second before real action, whereas they were required to type instantly after instruction in the second experiment. By analyzing spontaneous EEG signals between the instruction and the action, we predicted which finger was used. The prediction accuracy in the first experiment fell from 85% to 71% with the progress of experiment, the average accuracy being 78%, whereas the prediction result was almost random guess in the second experiment. The results demonstrate that (1) ERD patterns are significantly affected by the effective duration of motion imagination, (2) unconscious reduction of this duration can decrease the prediction accuracy. Therefore, when designing subsequent BCI experiments, we should devote our attention to the question of how to keep the effective duration of motion imagination.
Brain ; physiology ; Cortical Synchronization ; Electroencephalography ; Humans ; Man-Machine Systems ; Task Performance and Analysis ; User-Computer Interface

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.
Algorithms ; Event-Related Potentials, P300 ; physiology ; Evoked Potentials, Visual ; physiology ; Humans ; Pattern Recognition, Automated ; methods ; Principal Component Analysis ; Signal Processing, Computer-Assisted

Traditional sleep scoring system describes the sleep EEG characterized by features in time domain as well as frequency domain. Power Spectral Density (PSD) is one of the well-used methods to observe the occurrence of specified rhythms. However, the parameter model based PSD estimation is used with the assumption that the model order is determined as low as possible through prior knowledge. This paper briefs the development of Autoregressive Model Order (ARMO) criterion, and provides the distribution of ARMOs for specified sleep EEG, which shows that ARMOs concentrate on several well separated regions that are indicative of the microstructure and transition states. This study suggests the promising perspective of ARMO as a special EEG feature for weighing complexity, randomness and rhythm components.
Delta Rhythm ; Electroencephalography ; Humans ; Models, Neurological ; Regression Analysis ; Signal Processing, Computer-Assisted ; Sleep Stages ; physiology

Grey system theory was applied in analysis of Electroencephalogram (EEG) to extract features of driving fatigue in this study. Model GM(1,1) was built for EEG collected during simulative driving experiments. At the same time, the data of steering wheel movements and subjective fatigue level were analyzed as reference. The results of experiments reveal that the co-deviation of Model GM(1,1) parameter a and b, cov(a,b), coincides with the standard deviation of steering wheel movements. This indicates that Grey system theory is effective for EEG analysis and the parameters of GM(1,1) can well reflect the change of driving fatigue.
Adult ; Automobile Driving ; psychology ; Computer Simulation ; Electroencephalography ; methods ; Fatigue ; physiopathology ; Humans ; Male ; Models, Theoretical

The simulation of excitation propagation's process in human heart is one of the main aspects of ECG forward problem. The simulation results not only are the criterion of the simulation model's precision and reliability, but also have great value in researches and diagnoses. We performed the simulation of QRST waves of complete left bundle branch block (LBBB) and right bundle branch block (RBBB) in virtue of a vector propagation algorithm (VPA), which is accurate, efficient and applicable to anisotropic computer heart models. The simulation results accord with the actual QRST wave in clinical practice.
Algorithms ; Bundle-Branch Block ; pathology ; Computer Simulation ; Electrocardiography ; Humans ; Models, Cardiovascular ; Reproducibility of Results

Primary pigmented nodular adrenal disease (PPNAD) is a kind of autosomal dominant inherited disease. Patient in the study presented with Cushing's syndrome, and clinical and pathological diagnosis of PPNAD was confirmed. It is now confirmed that there are two relevant genes and their mutations may lead to PPNAD. This study showed no mutations in the patient, surpecting if there would be an alternative mechanism or a new gene in playing the role.

A new method of automatic detection of brain lesion based on wavelet feature vector of CT images has been proposed in the present paper. Firstly, we created training samples by manually segmenting normal CT images into gray matter, white matter and cerebrospinal fluid sub images. Then, we obtained the cluster centers using FCM clustering algorithm. When detecting lesions, the CT images to be detected was automatically segmented into sub images, with a certain degree of over-segmenting allowed under the premise of ensuring accuracy as much as possible. Then we extended these sub images and extracted the features to compute the distances with the cluster centers and to determine whether they belonged to the three kinds of normal samples, or, otherwise, belonged to lesions. The proposed method was verified by experiments.
Automatic Data Processing ; methods ; Brain ; diagnostic imaging ; Brain Neoplasms ; diagnostic imaging ; Humans ; Image Interpretation, Computer-Assisted ; methods ; Intracranial Hemorrhages ; diagnostic imaging ; Tomography, X-Ray Computed ; methods ; Wavelet Analysis