基于Legendre矩的CT及MR医学图象融合方法

为了提高CT、MR多模态医学图象配准、融合的精度和速度,提出了基于Legendre矩的CT和MR多模态医学图象配准、融合方法,并运用二维9数据图象的Legendre矩正交性和无冗余性的特点,通过找出CT及MR两种模态医学图象的质心,计算出两图象的比例因子,从而完成了两图象的平移和旋转,并精确地实现了CT和MR两模态图象的配信、融合,还优化了Legendre矩的快速算法和提高了应用Legendre矩配准CT和MR图象的速度。实验表明,利用Legendre矩对CT和MR等多模态图象配准、融合,不失为一种比较直接、简洁的方法;同时,Legendre矩在医学影象诊断、放疗计划系统等方面也具有重要的应用价值。     

Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering

Sparse representation (SR) has been widely used in image fusion in recent years. However, source image, segmented into vectors, reduces correlation and structural information of texture with conventional SR methods, and extracting texture with the sliding window technology is more likely to cause spatial inconsistency in flat regions of multi-modality medical fusion image. To solve these problems, a novel fusion method that combines separable dictionary optimization with Gabor filter in non-subsampled contourlet transform (NSCT) domain is proposed. Firstly, source images are decomposed into high frequency (HF) and low frequency (LF) components by NSCT. Then the HF components are reconstructed sparsely by separable dictionaries with iterative updating sparse coding and dictionary training. In the process, sparse coefficients and separable dictionaries are updated by orthogonal matching pursuit (OMP) and manifold-based conjugate gradient method, respectively. Meanwhile, the Gabor energy as weighting factor is utilized to guide the LF components fusion, and this further improves the fusion degree of low-significant feature in the flat regions. Finally, the fusion components are transformed to obtain fusion image by inverse NSCT. Experimental results demonstrate the more competitive results of the proposal, leading to the state-of-art performance on both visual quality and objective assessment.     

一种基于多模态模型的随机子空间分类集成算法

分类是当前机器学习的重要研究内容之一,已取得了一定的进展.现有的文本分类方法大多基于VSM模型,而VSM未能有效地利用隐含在文本中的结构信息.同时,VSM下的样本空间常常是高维的,单一的降维策略可能会丢失有用信息.为改进现有算法的不足,提出了一种基于多模态模型的随机子空间分类集成算法MMRFSEn,有效地利用文本中的结构信息（单词分布位置的均值和标准差）,且各基分类器是由随机选择的子空间构建而成.实验结果表明,该方法是有效可行的.     

异构信息空间中支持多模态融合实体搜索的多层时态数据模型

面对异构信息空间中具有时间信息的大量相互关联的异构实体数据如作者、论文、产品、电影等,提出一个以实体及关联关系为中心的多层的时态数据模型,即多层的时态实体关联网络MTE-Network,它能有效捕捉异构实体和关联关系的时间信息.基于此时态数据模型,提出了实体搜索的多模态融合的查询模型,其支持用户搜索异构信息空间中的任何类型的实体及相关实体,支持在实体级、实体聚类级和时间轴上的实体搜索,并且满足用户多模态融合实体搜索的信息需求.在真实数据集上的实验结果证明了该时态数据模型和查询模型的可行性和有效性.     

多模态图像配准的配准测度和性能分析

该文考察了多模态图像配准中几个典型的配准测度,揭示了它们之间的关系,通过实验对它们进行了全面的分析比较．实验中考察的测度主要包括:基于熵的测度、PIU测度以及PIU测度的改进形式等．实验结果显示了这几个典型配准测度在有效性和适应性方面的差异,为实际应用中合理选择这些测度提供可靠的参考．     

Study of three—dimensional PET and MR image registration based on higher—order mutual information

Mutual information has currently been one of the most intensively reserached measures.It has been proven to be accurate and effective registration measure.Despite the general promising results,mutual information sometimes smight lead to misregistration because of neglecting spatial information and treating intensity variations with undue sensitivity.In this paper,an extension of mutual information framework was proposed in which higher-order spatial information regarding image structures was incorporated into the registration processing of PET and MR.The second-order estimate of mutual information algorithm was applied to the registration of seven patients.Evaluation from Vanderbilt University and our visual inspection showed that sub-voxel accuracy and robust results were achieved in all cases with second-order mutual information as the similarity measure and with Powell‘s multidimensional direction set method as optimization strategy.     

Modeling Hong Kong’s stock index with the Student t-mixture autoregressive model

It is well known that financial returns are usually not normally distributed, but rather exhibit excess kurtosis. This implies that there is greater probability mass at the tails of the marginal or conditional distribution. Mixture-type time series models are potentially useful for modeling financial returns. However, most of these models make the assumption that the return series in each component is conditionally Gaussian, which may result in underestimates of the occurrence of extreme financial events, such as market crashes. In this paper, we apply the class of Student t-mixture autoregressive (TMAR) models to the return series of the Hong Kong Hang Seng Index. A TMAR model consists of a mixture of g autoregressive components with Student t-error distributions. Several interesting properties make the TMAR process a promising candidate for financial time series modeling. These models are able to capture serial correlations, time-varying means and volatilities, and the shape of the conditional distributions can be time-varied from short- to long-tailed or from unimodal to multi-modal. The use of Student t-distributed errors in each component of the model allows for conditional leptokurtic distribution, which can account for the commonly observed unconditional kurtosis in financial data.     

多靶分子影像学及其研究进展

多靶分子影像学是分子影像学极其重要的研究领域和发展方向.多靶分子影像学技术(多靶分子显像)包括多种显像剂-多靶分子显像、融合分子多靶显像、偶合分子多靶显像、多靶多功能分子显像.对多靶分子显像及其显像模式进行了较详尽阐述,尤其是对多靶正电子发射断层(PET)显像进行了较深入的探讨与研究.     

Rank methods for the analysis of clustered data in diagnostic trials

Methods for comparing areas under receiver operating characteristic curves usually depend on the assumption of independence between diseased and nondiseased units in the trial. However, if several parts of the same subject have to be classified as diseased or nondiseased, such observations are no longer independent. This situation is referred to as clustered data. First ideas for the analysis of such data are based on the theory of U-statistics. The idea of the multivariate nonparametric Behrens-Fisher problem is extended to clustered data and to factorial designs. ANOVA-type statistics are suggested for both small and moderate sample sizes. They are evaluated in a simulation study and applied to a real-data example.