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Search Results: 1 - 10 of 55245 matches for " 戴亚康 "
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实时交互的自由臂三维超声成像系统
,田捷?,薛健?
软件学报 , 2006,
Abstract: 传统的自由臂三维超声成像系统不能做到实时交互的重建.介绍了实时交互的自由臂三维超声成像系统rif-3dusis(real-timeandinteractivefreehandthree-dimensionalultrasoundimagingsystem),包括其整体设计思想、三维重建方法和软硬件系统结构.rif-3dusis的特点是:(1)在扫描过程中,实时进行采集和三维重建,并使得操作者能够对重建体模型进行实时的交互操作;(2)在扫描的同时,不仅通过动态显示重建体提供可视化的反馈信息,而且不断更新显示已重建的比率,以定量地引导扫描重建的进度;(3)在每一步操作之前都会给出提示信息,引导操作者进行操作.实验结果表明了rif-3dusis实时重建的有效性和动态交互的灵活性.
海量医学数据处理框架及快速体绘制算法
薛健?,田捷?,,陈健?
软件学报 , 2008,
Abstract: 设计并实现了一套针对海量数据的处理和分析算法框架,并将其融入实验室早先开发完成的医学影像算法研发平台mitk(medicalimagingtoolkit)中,真正建立起一个海量医学影像数据的处理平台,并在此基础上研究了针对海量数据的基于光线投射和三维纹理的快速体绘制算法,提出了一种半自适应分块的方法对原始数据进行分块,在不对分块速度产生太大影响的基础上得到了更好的分块结果,同时使用图形硬件来进一步加速整个算法的绘制流程.实验结果表明了该平台和算法对于海量医学数据处理和可视化的有效性.
多速度函数水平集算法及在医学分割中的应用
陈健?,田捷?,薛健?,
软件学报 , 2007,
Abstract: 以往的水平集算法都只有一个单一的速度函数,在零水平集的演化过程中,能量函数最小化是一个很复杂的过程,而单一的速度函数存在很多问题.在此基础上,根据不同分割区域属性的异同,提出了一种具有多个速度函数的多水平集分割算法:以不同的待分割区域构造多个不同的水平集函数,相应地构造多个不同的速度函数.多个零水平集同时演化,相互作用,以达到分割的目的.该方法不但提高了分割的精度,而且能够很好地解决单一速度函数水平集算法难以处理的边界缺口问题.将此算法应用于医学mri和ct的图像分割,得到了很好的分割结果.
基于栈式深度多项式网络集成学习框架的帕金森病计算机辅助诊断
Computer-aided diagnosis of Parkinson's disease based on the stacked deep polynomial networks ensemble learning framework

陈璐,施俊,彭博,
- , 2018, DOI: 10.7507/1001-5515.201709030
Abstract: 特征表达是基于磁共振成像(MRI)的帕金森病(PD)计算机辅助诊断系统诊断准确性的重要决定因素。深度多项式网络(DPN)是一种新的有监督深度学习算法,对于小数据集具有良好的特征表达能力。本文提出一种面向 PD 计算机辅助诊断的栈式 DPN(SDPN)集成学习框架,以有效提高基于小数据的 PD 辅助诊断准确性。本框架对所提取的 MRI 特征的每一个特征子集分别通过 SDPN 得到新的特征表达,然后采用支持向量机(SVM)对每个子集进行分类,再对所有分类器进行集成学习,得到最终的 PD 诊断结果。通过对公开的帕金森病数据库 PPMI 进行实验,基于脑网络特征的分类精度、敏感度和特异性分别为 90.15%、85.48% 和 93.27%;而基于多视图脑区特征的分类精度、敏感度和特异性分别为 87.18%、86.90% 和 87.27%。与在 PPMI 数据库中的 MRI 数据集进行实验的其他算法研究相比,本文所提出的算法获得了更好的分类结果。本文研究表明了所提出的 SDPN 集成学习框架的有效性,具有应用于 PD 计算机辅助诊断的可行性。
Feature representation is the crucial factor for the magnetic resonance imaging (MRI) based computer-aided diagnosis (CAD) of Parkinson’s disease (PD). Deep polynomial network (DPN) is a novel supervised deep learning algorithm, which has excellent feature representation for small dataset. In this work, a stacked DPN (SDPN) based ensemble learning framework is proposed for diagnosis of PD, which can improve diagnostic accuracy for small dataset. In the proposed framework, SDPN was performed on each subset of extracted features from MRI images to generate new feature representation. The support vector machine (SVM) was then adopted to perform classification task on each subset. The ensemble learning algorithm was then performed on all the SVM classifiers to generate the final diagnosis for PD. The experimental results on the Parkinson’s Progression Markers Initiative dataset (PPMI) showed that the proposed algorithm achieved the classification accuracy, sensitivity and specificity of 90.15%, 85.48% and 93.27%, respectively, with the brain network features, and it also got the classification accuracy of 87.18%, sensitivity of 86.90% and specificity of 87.27% on the multi-view features extracted from different brain regions. Moreover, the proposed algorithm outperformed other algorithms on the MRI dataset from PPMI. It suggests that the proposed SDPN-based ensemble learning framework has the feasibility and effectiveness for the CAD of PD.
甘肃张掖市冬季气温变化的时空特征
张勃,淑媛,刘艳艳,王海军,,声佩
地理研究 , 2010, DOI: 10.11821/yj2010010014
Abstract: 依据张掖市近50年来的冬季气温观测资料,运用Mann-Kendall法、小波分析、空间插值等方法,对张掖市冬季气温的时空分布规律和变化周期进行分析。结果表明冬季气温总体上呈现增暖的趋势(β值为0.08),线性增长率为0.56℃/10a,相当于近50年冬季气温升高了2.8℃,冬季增温对全年升温的贡献率高达89%。1985年冬季气温发生突变,之后进入偏暖期,1987年后增温趋势更加显著。冬季气温存在10年左右和22年左右的振荡周期,其中22年左右的振荡周期较强。冬季气温空间分布不均,呈现出由东南向西北逐渐增温的趋势。冬季气温从20世纪70年代起就开始增温,东部增温速度明显高于西部,冬季气温增暖主要发生在最近的20余年内。
河西地区1960年至2008年潜在蒸发量的时空变化分析
,,,声佩,王海军,郭铃霞,淑媛
资源科学 , 2010,
Abstract: 利用FAOPenman-Monteith模型计算出潜在蒸发量,运用数理统计理论和GIS空间分析技术,对河西地区潜在蒸发的时空特征进行了分析,结合Morlet小波分析和Hurst指数预测潜在蒸发变化趋势。结果表明:①河西地区潜在蒸发总体呈波动下降趋势,20世纪60年代~90年代,潜在蒸发为稳定下降期,约2.76mm/年,其中70年代减少率最大,约11.3mm/年;夏季潜在蒸发变化速率最大,约-2.07mm/年,其次为春季和秋季,冬季最小;②河西地区潜在蒸发自西北向东南呈递减趋势,靠近祁连山地区最小;潜在蒸发主要集中在春季和夏季,分别占年潜在蒸发30%和40%,秋季次之,冬季最小;③影响河西地区潜在蒸发的主要因素为风速,而影响春季潜在蒸发的主要因素是气温;④河西地区潜在蒸发存在12年左右的主振荡周期,年潜在蒸发表现出比较强的持续性,即未来趋势与近12年变化趋势的一致,潜在蒸发呈上升趋势。
Levelset Method with Multi-Speed-Function and Its Application in Segmentation of Medical Images
多速度函数水平集算法及在医学分割中的应用

CHEN Jian,TIAN Jie,XUE Jian,DAI Ya-Kang,
陈健
,田捷,薛健,

软件学报 , 2007,
Abstract: All of the former level set algorithms have only one level set function and only one speed function, and it is a complex procedure to minimize the energy function during the evolvement of the zero-level-set. Furthermore, there are a lot of problems in this single speed function. In this paper, a new multi-level-set algorithm with multiplicate speed functions is proposed according to the different properties of different objects: Different level set functions are constructed in different regions, and so are different speed functions accordingly; many zero-level-sets are evolved at the same time and act on one another in order to segment. This method not only enhances the accuracy of segmentation, but also solves the bounder gap problem well, which is quite a puzzle for single level set algorithm. Perfect results are achieved when this method is applied to segment the MR and CT images.
Processing Framework and the Fast Volume Rendering Algorithms for Out- of-Core Medical Data
海量医学数据处理框架及快速体绘制算法

XUE Jian,TIAN Jie,DAI Ya-Kang,CHEN Jian,
薛健
,田捷,,陈健

软件学报 , 2008,
Abstract: This paper designs and implements an algorithm framework for the out-of-core medical data processing and analyzing and integrates it into MITK (medical imaging toolkit), an algorithm toolkit for medical image processing and analyzing accomplished by the group. With the help of this, a processing platform for the out-of-core medical data is set up and fast out-of-core volume rendering algorithms based on volume ray casting and 3D texture are studied in this paper. A semi-adaptive partitioning method is proposed to divide original data sets into sub-blocks and get a better partitioning result without influencing the partitioning speed. Furthermore, the graphics hardware is also used to accelerate the rendering process. The experimental results indicate that the new framework and algorithms are effective and efficient for the processing and visualization of the out-of-core medical data sets.
基于功率谱的睡眠中癫痫发作预测
Prediction of seizures in sleep based on power spectrum

刘伟楠,刘燕,佟宝同,赵凌霄,杨莹雪,王玉平,
- , 2018, DOI: 10.7507/1001-5515.201708062
Abstract: 睡眠中如果癫痫发作会增加患者并发症发作和猝死的概率,有效预测患者睡眠中的癫痫发作可让医患及时采取措施,降低上述概率。现有癫痫发作预测方法多是基于脑电信号设计的,但并未在睡眠时期进行针对性研究,而该时期脑电信号相比其他时期有其特殊性,因此为提高灵敏度、降低错误报警率,本文将挖掘睡眠脑电信号的特点,研究睡眠中癫痫发作的预测方法。本文提出的方法中首先构建特征向量,包括不同波段的绝对功率谱、相对功率谱和功率谱比值;其次应用分离性判据和分支定界法进行特征选择;最后训练支持向量机分类器并实现预测。相比于不针对睡眠脑电信号特点的癫痫预测方法(灵敏度 91.67%,错误报警率 9.19%),本文方法的灵敏度(100%)有所提高,而错误报警率(2.11%)则有所降低。本文方法是对现有癫痫预测方法的补充,具有一定的临床价值。
Seizures during sleep increase the probability of complication and sudden death. Effective prediction of seizures in sleep allows doctors and patients to take timely treatments to reduce the aforementioned probability. Most of the existing methods make use of electroencephalogram (EEG) to predict seizures, which are not specific developed for the sleep. However, EEG during sleep has its characteristics compared with EEG during other states. Therefore, in order to improve the sensitivity and reduce the false alarm rate, this paper utilized the characteristics of EEG to predict seizures during sleep. We firstly constructed the feature vector including the absolute power spectrum, the relative power spectrum and the power spectrum ratio in different frequencies. Secondly, the separation criterion and branch-and-bound method were applied to select features. Finally, support vector machine classifier were trained, which is then employed for online prediction. Compared with the existing method that do not consider the characteristics of sleeping EEG (sensitivity 91.67%, false alarm rate 9.19%), the proposed method was superior in terms of sensitivity (100%) and false alarm rate (2.11%). This method can improve the existing epilepsy prediction methods and has important clinical value.
贝叶斯规整化神经网络预测苯取代物的毒性

中南民族大学学报(自然科学版) , 2007,
Abstract: 利用贝叶斯规整化神经网络(BRNN)研究了59种苯取代的环境毒性和分子结构的关系.建立了基于脂水分配系数和4种量子指数的QSAR环境毒性预测模型.结果表明:该模型的预测性能超过偏最小二乘法(PLS)和逐步线性回归方法,残差分析显示其稳定可靠,有望成为一种良好的苯取代物毒性的环境评价和预测的方法.
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