Micro Insert: A Prototype Full-Ring PET Device for Improving the Image Resolution of a Small-Animal PET Scanner

A full-ring PET insert device should be able to enhance the image resolution of existing small-animal PET scanners. Methods: The device consists of 18 high-resolution PET detectors in a cylindric enclosure. Each detector contains a cerium-doped lutetium oxyorthosilicate array (12 x 12 crystals, 0.72 x 1.51 x 3.75 mm each) coupled to a position-sensitive photomultiplier tube via an optical fiber bundle made of 8 x 16 square multiclad fibers. Signals from the insert detectors are connected to the scanner through the electronics of the disabled first ring of detectors, which permits coincidence detection between the 2 systems. Energy resolution of a detector was measured using a Ge-68 point source, and a calibrated 68Ge point source stepped across the axial field of view (FOV) provided the sensitivity profile of the system. A Na-22 point source imaged at different offsets from the center characterized the in-plane resolution of the insert system. Imaging was then performed with a Derenzo phantom filled with 19.5 MBq of F-18-fluoride and imaged for 2 h; a 24.3-g mouse injected with 129.5 MBq of F-18-fluoride and imaged in 5 bed positions at 3.5 h after injection; and a 22.8-g mouse injected with 14.3 MBq of F-18-FDG and imaged for 2 h with electrocardiogram gating. Results: The energy resolution of a typical detector module at 511 keV is 19.0% +/- 3.1 %. The peak sensitivity of the system is approximately 2.67%. The image resolution of the system ranges from 1.0- to 1.8-mm full width at half maximum near the center of the FOV, depending on the type of coincidence events used for image reconstruction. Derenzo phantom and mouse bone images showed significant improvement in transaxial image resolution using the insert device. Mouse heart images demonstrated the gated imaging capability of the device. Conclusion: We have built a prototype full-ring insert device for a small-animal PET scanner to provide higher-resolution PET images within a reduced imaging FOV. Development of additional correction techniques are needed to achieve quantitative imaging with such an insert.

Rotating wheel filter design and optimization for a uniform depth dose distribution in heavy ion therapy

Treatment planning of heavy-ion radiotherapy involves predictive calculation of not only the physical dose but also the biological dose in a patient body. The goal in designing beam-modulating devices for heavy ion therapy is to achieve uniform biological effects across the spread-out Bragg peak (SOBP). To achieve this, a mathematical model of Bragg peak movement is presented. The parameters of this model have been resolved with Monte Carlo method. And a rotating wheel filter is designed basing on the velocity of the Bragg peak movement.

低能He~(2+)入射He原子转移电离实验中出射电子成像研究；Momentum image of emission electrons in transfer ionization process of slow He~(2+) colliding on He

利用反应显微谱仪对70keV He2+-He转移电离过程中的出射电子进行了成像,研究了出射电子的空间速度分布特征.结果表明:电子主要集中在散射平面内;在散射平面内,电子速度分布介于零与入射离子速度Vp之间(即前向出射)且在散射离子和靶核核间轴处有一极小值,呈现出典型的双峰结构.出射电子的上述分布特征可由出射电子波函数σ振幅和π振幅的干涉进行定性解释,σ振幅和π振幅对出射电子波函数的贡献与碰撞参数相关.在小碰撞参数下,π振幅的贡献更加明显;而在大碰撞参数下,σ振幅的贡献更加显著.

Relationship between precipitation and the infiltration depth over the middle and lower reaches of the Yellow River and Yangtze-Huaihe River Valley

National Natural Science Foundation of China [40701021, 40625002, 40331013]; National Knowledge Innovation Program of Chinese Academy of Sciences [KZCX2-YW-315-2]

Local histogram based geometric invariant image watermarking

Compared with other existing methods, the feature point-based image watermarking schemes can resist to global geometric attacks and local geometric attacks, especially cropping and random bending attacks (RBAs), by binding watermark synchronization with salient image characteristics. However, the watermark detection rate remains low in the current feature point-based watermarking schemes. The main reason is that both of feature point extraction and watermark embedding are more or less related to the pixel position, which is seriously distorted by the interpolation error and the shift problem during geometric attacks. In view of these facts, this paper proposes a geometrically robust image watermarking scheme based on local histogram. Our scheme mainly consists of three components: (1) feature points extraction and local circular regions (LCRs) construction are conducted by using Harris-Laplace detector; (2) a mechanism of grapy theoretical clustering-based feature selection is used to choose a set of non-overlapped LCRs, then geometrically invariant LCRs are completely formed through dominant orientation normalization; and (3) the histogram and mean statistically independent of the pixel position are calculated over the selected LCRs and utilized to embed watermarks. Experimental results demonstrate that the proposed scheme can provide sufficient robustness against geometric attacks as well as common image processing operations. (C) 2010 Elsevier B.V. All rights reserved.

Partially Supervised Neighbor Embedding for Example-Based Image Super-Resolution

Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding.

An integrated aurora image retrieval system: AuroraEye

With the digital all-sky imager (ASI) emergence in aurora research, millions of images are captured annually. However, only a fraction of which can be actually used. To address the problem incurred by low efficient manual processing, an integrated image analysis and retrieval system is developed. For precisely representing aurora image, macroscopic and microscopic features are combined to describe aurora texture. To reduce the feature dimensionality of the huge dataset, a modified local binary pattern (LBP) called ALBP is proposed to depict the microscopic texture, and scale-invariant Gabor and orientation-invariant Gabor are employed to extract the macroscopic texture. A physical property of aurora is inducted as region features to bridge the gap between the low-level visual features and high-level semantic description. The experiments results demonstrate that the ALBP method achieves high classification rate and low computational complexity. The retrieval simulation results show that the developed retrieval system is efficient for huge dataset. (c) 2010 Elsevier Inc. All rights reserved.

A Unified Tensor Level Set for Image Segmentation

This paper presents a new region-based unified tensor level set model for image segmentation. This model introduces a three-order tensor to comprehensively depict features of pixels, e.g., gray value and the local geometrical features, such as orientation and gradient, and then, by defining a weighted distance, we generalized the representative region-based level set method from scalar to tensor. The proposed model has four main advantages compared with the traditional representative method as follows. First, involving the Gaussian filter bank, the model is robust against noise, particularly the salt-and pepper-type noise. Second, considering the local geometrical features, e. g., orientation and gradient, the model pays more attention to boundaries and makes the evolving curve stop more easily at the boundary location. Third, due to the unified tensor pixel representation representing the pixels, the model segments images more accurately and naturally. Fourth, based on a weighted distance definition, the model possesses the capacity to cope with data varying from scalar to vector, then to high-order tensor. We apply the proposed method to synthetic, medical, and natural images, and the result suggests that the proposed method is superior to the available representative region-based level set method.

Geometric Distortion Insensitive Image Watermarking in Affine Covariant Regions

Feature-based image watermarking schemes, which aim to survive various geometric distortions, have attracted great attention in recent years. Existing schemes have shown robustness against rotation, scaling, and translation, but few are resistant to cropping, nonisotropic scaling, random bending attacks (RBAs), and affine transformations. Seo and Yoo present a geometrically invariant image watermarking based on affine covariant regions (ACRs) that provide a certain degree of robustness. To further enhance the robustness, we propose a new image watermarking scheme on the basis of Seo's work, which is insensitive to geometric distortions as well as common image processing operations. Our scheme is mainly composed of three components: 1) feature selection procedure based on graph theoretical clustering algorithm is applied to obtain a set of stable and nonoverlapped ACRs; 2) for each chosen ACR, local normalization, and orientation alignment are performed to generate a geometrically invariant region, which can obviously improve the robustness of the proposed watermarking scheme; and 3) in order to prevent the degradation in image quality caused by the normalization and inverse normalization, indirect inverse normalization is adopted to achieve a good compromise between the imperceptibility and robustness. Experiments are carried out on an image set of 100 images collected from Internet, and the preliminary results demonstrate that the developed method improves the performance over some representative image watermarking approaches in terms of robustness.

Local Feature Based Geometric-Resistant Image Information Hiding

Watermarking aims to hide particular information into some carrier but does not change the visual cognition of the carrier itself. Local features are good candidates to address the watermark synchronization error caused by geometric distortions and have attracted great attention for content-based image watermarking. This paper presents a novel feature point-based image watermarking scheme against geometric distortions. Scale invariant feature transform (SIFT) is first adopted to extract feature points and to generate a disk for each feature point that is invariant to translation and scaling. For each disk, orientation alignment is then performed to achieve rotation invariance. Finally, watermark is embedded in middle-frequency discrete Fourier transform (DFT) coefficients of each disk to improve the robustness against common image processing operations. Extensive experimental results and comparisons with some representative image watermarking methods confirm the excellent performance of the proposed method in robustness against various geometric distortions as well as common image processing operations.

Image Ratio Features for Facial Expression Recognition Application

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e. g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.