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Your search keyword '"Wang, Boliang"' showing total 28 results
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28 results on '"Wang, Boliang"'

2. An Algorithm Embedded Prior Shape Information for Segmentation of Medical Image Series

Author

Huang-Xiaoyang, Wang-Boliang, Zheng-Chaowei, Huang-shaohui, Dai-Peishan, and Min-xiaoping

Subjects
Image Series, Active contour model, business.industry, Segmentation-based object categorization, Physics::Medical Physics, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Scale-space segmentation, Pattern recognition, Image segmentation, Active appearance model, Image texture, Computer Science::Computer Vision and Pattern Recognition, Active shape model, Computer vision, Artificial intelligence, business, Algorithm, ComputingMethodologies_COMPUTERGRAPHICS, Mathematics
Abstract

An algorithm based on the active shape model is proposed for the segmentation of medical image series. The edge of soft tissues in medical image usually are not very clearly, so it's difficult to segment them only by the computer. But a tissue's shape deform sequentially in medical image series. We selected some images from the image series and segmented them with the help of doctors, the results of the segmentation was used as the training maps to create an active shape model by PCA Algorithm. We used this model to segment remainder images in the image sequence by mutual information registration and active contour model. The experiments show the efficiency of this method.

Published
2007

3. Stimulation frequency extraction in SSVEP-based brain-computer interface

Author

Gao Xiaorong, Wang Boliang, Gao Shangkai, and Cheng Ming

Subjects
Steady state (electronics), genetic structures, medicine.diagnostic_test, business.industry, Computer science, Interface (computing), Speech recognition, Pattern recognition, Electroencephalography, Neurophysiology, Visual cortex, medicine.anatomical_structure, medicine, Electrode array, Artificial intelligence, Evoked potential, business, Brain–computer interface
Abstract

A periodogram-based method is used to extract the stimulation frequency in a brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). In the system, tens of buttons illuminated at different frequencies are used to generate deterministic sinusoidal responses or SSVEP at the visual cortex, which are derived from the electroencephalogram (EEG) by a suitable electrode array. Based on the periodogram of a time-series, we test the EEG data for the presence of hidden periodic components, which correspond to SSVEP, and extract the stimulation frequencies. The method performs well in simulation, and is applied successfully to real data.

Published
2005

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