We propose a novel palm-vein recognition model based on the end-to-end convolutional neural network. In this model, the convolutional layer and the pooling layer were alternately connected to extract the image features, and the categorical attribute was estimated simultaneously via the neural network classifier. The classification error was minimized via the mini-batch stochastic gradient descent algorithm with momentum to optimize the feature descriptor along with the direction of the gradient descent. Four strategies including data augmentation, batch normalization, dropout, and L2 parameter regularization were applied in the model to reduce the generalization error. The experimental results showed that for classifying 500 subjects form PolyU database and a self-established database, this model achieved identification rates of 99.90% and 98.05%, respectively, with an identification time for a single sample less than 9 ms. The proposed approach, as compared with the traditional method, could improve the accuracy of palm vein recognition in clincal applications and provides a new approach to palm vein recognition.
Algorithms ; Databases, Factual ; Hand ; blood supply ; diagnostic imaging ; Humans ; Neural Networks (Computer) ; Veins ; diagnostic imaging

Four-dimensional cone beam CT (4D-CBCT) imaging can provide accurate location information of real-time breathing for imaging-guided radiotherapy. How to improve the accuracy of 4D-CBCT reconstruction image is a hot topic in current studies. PICCS algorithm performs remarkably in all 4D-CBCT reconstruction algorithms based on CS theory. The improved PICCS algorithm proposed in this paper improves the prior image on the basis of the traditional PICCS algorithm. According to the location information of each phase, the corresponding prior image is constructed, which completely eliminates the motion blur of the reconstructed image caused by the mismatch of the projection data. Meanwhile, the data fidelity model of the proposed method is consistent with the traditional PICCS algorithm. The experimental results showed that the reconstructed image using the proposed method had a clearer organization boundary compared with that of images reconstructed using the traditional PICCS algorithm. This proposed method significantly reduced the motion artifact and improved the image resolution.
Algorithms ; Cone-Beam Computed Tomography ; methods ; Four-Dimensional Computed Tomography ; Humans ; Image Processing, Computer-Assisted ; Organ Motion ; Radiographic Image Enhancement ; instrumentation ; methods ; Respiration

Objective To achieve low-dose digital breast tomosynthsis (DBT) projection recovery using penalized weighted least square algorithm incorporating accurate modeling of the variance of the projection data and noise correlation in the flat panel detector. Methods Models were established for the quantal noise and electronic noise in the DBT system to construct the penalized weighted least squares algorithm based on noise correlation for projection data restoration. The filter back projection algorithm was then used for DBT image reconstruction. Results The reconstruction results of the ACR phantom data at different dose levels showed a good performance of the proposed method in noise suppression and detail preservation. CNRs and LSNRs of the reconstructed images from the restored projections were increased by about 3.6 times compared to those of reconstructed images from the original projections. Conclusion The proposed method can significantly reduce noise and improve the quality of DBT images.

Objective To achieve low-dose digital breast tomosynthsis (DBT) projection recovery using penalized weighted least square algorithm incorporating accurate modeling of the variance of the projection data and noise correlation in the flat panel detector. Methods Models were established for the quantal noise and electronic noise in the DBT system to construct the penalized weighted least squares algorithm based on noise correlation for projection data restoration. The filter back projection algorithm was then used for DBT image reconstruction. Results The reconstruction results of the ACR phantom data at different dose levels showed a good performance of the proposed method in noise suppression and detail preservation. CNRs and LSNRs of the reconstructed images from the restored projections were increased by about 3.6 times compared to those of reconstructed images from the original projections. Conclusion The proposed method can significantly reduce noise and improve the quality of DBT images.

We propose a multi-weighted probabilistic atlas to obtain accurate, robust, and reliable segmentation. The local similarity measure is used as the weight to compute the probabilistic atlas, and the distance field is used as the weight to incorporate the locality information of the atlas; the self-similarity is used as the weight to incorporate the local information of target image to refine the probabilistic atlas. Experimental results with brain MRI images showed that the proposed algorithm outperforms the common brain image segmentation methods and achieved a median Dice coefficient of 87.1% on the left hippocampus and 87.6% on the right.
Algorithms ; Brain ; anatomy & histology ; Humans ; Magnetic Resonance Imaging ; Neuroimaging

OBJECTIVEWe propose a cross-correlation method for automatic extraction of the pennation angle (PA) of the gastrocnemius (GM) muscle from ultrasound radiofrequency (RF) signals.

METHODSThe ultrasound RF signals of the GM muscles in tension condition from normal subjects and the simulated ultrasound signals were collected. After the starting point of tracking, a fascicle was selected in the reconstructed GM ultrasound image from the RF signals, and the fascicle and deep aponeurosis could be automatically tracked using the cross-correlation algorithm. The lines of the fascicle and deep aponeurosis were then drawn and the PA was calculated. The reproducibility of the proposed method and its consistency with the manual measurement method were tested.

RESULTSThe angles of the simulated fascicles were precisely extracted automatically. The difference between the experimental measurement and the theoretical values was less than 1°. The PA measured automatically and manually was 20.48°∓0.47° and 21.49°∓1.79°, respectively. The coefficient of variation (CV) of the two methods was less than 3% and the root-mean square error (RMSE) was less than 1°. Bland-Altman plot showed a good agreement between the proposed automatic method and the manual method.

CONCLUSIONThe proposed cross-correlation automatic measurement method can detect the orientation of the fascicle and deep aponeurosis and measure the PA based on ultrasound RF signals with serious speckle noise.

OBJECTIVETo propose a new method for automatic segmentation of manually determined knee articular cartilage into 9 subregions for T2 measurement.

METHODSThe middle line and normal line were automatically obtained based on the outline of articular cartilage manually drawn by experienced radiologists. The region of articular cartilage was then equidistantly divided into 3 layers along the direction of the normal line, and each layer was further equidistantly divided into 3 segments along the direction of the middle line. Finally the mean T2 value of each subregion was calculated. Bland-Altman analysis was used to evaluate the agreement between the proposed and manual subregion segmentation methods.

RESULTSThe 95% limits of agreement of manual and automatic methods ranged from -3.04 to 3.20 ms, demonstrating a narrow 95% limits of agreement (less than half of the minimum average). The coefficient of variation between the manual and proposed subregion methods was 4.04%.

CONCLUSIONThe proposed subregion segmentation method shows a good agreement with the manual segmentation method and minimizes potential subjectivity of the manual method.

The coupling from different elements of the multi-channel coil leads to the splitting of the resonance frequency and deviation from the Lamor frequency. Decoupling between different elements is the key technology in the design of the radiofrequency (RF) coil. The electrical decoupling circuits should vary with different arrangements of the elements. A novel method of decoupling for the RF coil used in the intraoperative MR-guided focused ultrasound system is reported in the paper. The prototype RF coil was made according to the proposed decoupling method. The bench test of the prototype showed that the performance of the decoupling of the coil was excellent. The images in vivo were acquired with the designed prototype RF coil.
Electric Conductivity ; Equipment Design ; Humans ; Intraoperative Care ; Magnetic Resonance Imaging ; instrumentation ; Pulsed Radiofrequency Treatment ; instrumentation ; Radio Waves

Affine motion is common during PROPELLER magnetic resonance imaging (MRI) in abdomen or other soft tissues. The current algorithm, up till now, for affine motion compensation is based on frequency domain, which compensates the motion in k space and then reconstruct the final image based on gridding method. But aliasing and some tiny artifacts may exist. This paper proposed a new algorithm for affine motion compensation based on image domain. Firstly, exact affine motion information was obtained through the image registration, secondly k space coordinate was corrected for compensating the k space strips sampling density, then the images obtained from inverse FFT was compensated using motion information, finally the final results were composited after rotation. The experimental results showed that the proposed method could more effectively suppress the motion artifacts compared to the current algorithm.
Abdomen ; pathology ; Algorithms ; Artifacts ; Fourier Analysis ; Humans ; Image Processing, Computer-Assisted ; methods ; Magnetic Resonance Imaging ; methods ; Movement

Objective: 3D segmentation is an important part of medical image analysis and visualization. It also continues to be large challenge in the medical image segmentation. While level sets have demonstrated a great potential for 3D medical image segmentation, these algorithms have a large computational burden thus are not suitable for real time processing requirement. To solve this problem, we propose a parallel accerelated method based on CUDA. Methods: We implement C-V level set algorithm in the CUDA environment which is the NVIDIA's GPGPU model.The segmentation speed can greatly improved by using independence of image pixel and concurrence of partial differential equation .The paper shows the flow chart of the parallel computing and gives the detailed introduction of the C-V level set algorithm which is implemented in the CUDA environment. Results: Realizing the C-V level set parallel accerelated algorithm. This method has faster segmentation speed while preserving the qualitative results, Conclusions: This method is viable and makes the fast 3D medical image segmentation come hue.