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| 1 | Tissue level microstructure and mechanical properties of the femoral head in the proximal femur of fracture patients显示文摘This study aims to investigate the regional variations of trabecular morphological parameters and mechanical parameters of the femoral head,as well as to determine the relationship between trabecular morphological and mechanical parameters.Seven femoral heads from patients with fractured proximal femur were scanned using a micro-CT system.Each femoral head was divided into 12 sub-regions according to the trabecular orientation.One 125 mm^3 trabecular cubic model was reconstructed from each sub-region.A total of 81 trabecular models were reconstructed,except three destroyed sub-regions from two femoral heads during the surgery.Trabecular morphological parameters,i.e.trabecular separation(Tb.Sp),trabecular thickness(Tb.Th),specific bone surface(BS/B V),bone volume fraction(BV/TV),structural model index(SMI),and degree of anisotropy(DA) were measured.Micro-finite element analyses were performed for each cube to obtain the apparent Young's modulus and tissue level von Mises stress distribution under 1%compressive strain along three orthogonal directions,respectively.Results revealed significant regional variations in the morphological parameters(P<0.05).Young's moduli along the trabecular orientation were significantly higher than those along the other two directions.In general,trabecular mechanical properties in the medial region were lower than those in the lateral region.Trabecular mechanical parameters along the trabecular orientation were significantly correlated with BS/BV,BV/TV,Tb.Th,and DA.In this study,regional variations of microstructural features and mechanical properties in the femoral head of patients with proximal femur fracture were thoroughly investigated at the tissue level.The results of this study will help to elucidate the mechanism of femoral head fracture for reducing fracture risk and developing treatment strategies for the elderly. | Linwei Lü Guangwei Meng He Gong Dong Zhu Jiazi Gao Yubo Fan | 2015 | Acta Mechanica Sinica2015，31，2： | 4 |
| 2 | Brief review of image denoising techniques显示文摘With the explosion in the number of digital images taken every day,the demand for more accurate and visually pleasing images is increasing.However,the images captured by modern cameras are inevitably degraded by noise,which leads to deteriorated visual image quality.Therefore,work is required to reduce noise without losing image features(edges,corners,and other sharp structures).So far,researchers have already proposed various methods for decreasing noise.Each method has its own advantages and disadvantages.In this paper,we summarize some important research in the field of image denoising.First,we give the formulation of the image denoising problem,and then we present several image denoising techniques.In addition,we discuss the characteristics of these techniques.Finally,we provide several promising directions for future research. | Linwei Fan Fan Zhang Hui Fan Caiming Zhang | 2019 | Visual Computing for Industry,Biomedicine,and Art2019，2，1： | 2 |
| 3 | A flexible technique to select objects via convolutional neural network in VR space显示文摘Most studies on the selection techniques of projection-based VR systems are dependent on users wearing complex or expensive input devices, however there are lack of more convenient selection techniques.In this paper, we propose a flexible 3 D selection technique in a large display projection-based virtual environment. Herein, we present a body tracking method using convolutional neural network(CNN) to estimate3 D skeletons of multi-users, and propose a region-based selection method to effectively select virtual objects using only the tracked fingertips of multi-users. Additionally, a multi-user merge method is introduced to enable users’ actions and perception to realign when multiple users observe a single stereoscopic display.By comparing with state-of-the-art CNN-based pose estimation methods, the proposed CNN-based body tracking method enables considerable estimation accuracy with the guarantee of real-time performance. In addition, we evaluate our selection technique against three prevalent selection techniques and test the performance of our selection technique in a multi-user scenario. The results show that our selection technique significantly increases the efficiency and effectiveness, and is of comparable stability to support multi-user interaction. | Huiyu LI Linwei FAN | 2020 | Science China(Information Sciences)2020，63，1： | 1 |
| 4 | Nonlocal image denoising using edge-based similarity metric and adaptive parameter selection显示文摘Dear editor,We propose a nonlocal image denoising method with an edge-based similarity metric and adaptive parameter selection.The proposed denoising method uses a two-stage scheme to refine the denoising results.It first produces the central | Linwei FAN Xuemei LI Qiang GUO Caiming ZHANG | 2018 | Science China(Information Sciences)2018，61，4： | 1 |
| 5 | Filter-cluster attention based recursive network for low-light enhancement显示文摘The poor quality of images recorded in low-light environments affects their further applications.To improve the visibility of low-light images,we propose a recurrent network based on filter-cluster attention(FCA),the main body of which consists of three units:difference concern,gate recurrent,and iterative residual.The network performs multi-stage recursive learning on low-light images,and then extracts deeper feature information.To compute more accurate dependence,we design a novel FCA that focuses on the saliency of feature channels.FCA and self-attention are used to highlight the low-light regions and important channels of the feature.We also design a dense connection pyramid(DenCP)to extract the color features of the low-light inversion image,to compensate for the loss of the image's color information.Experimental results on six public datasets show that our method has outstanding performance in subjective and quantitative comparisons. | Zhixiong HUANG Jinjiang LI Zhen HUA Linwei FAN | 2023 | Frontiers of Information Technology & Electronic Engineering2023，24，7： | 0 |
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