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医学步态分析中的复杂场景下运动目标检测技术 总被引:1,自引:0,他引:1
针对医学步态分析中的复杂场景下运动目标检测问题,提出了基于贝叶斯决策规则的方法.该方法由变化检测、变化分类、前景目标提取和背景更新四部分组成.变化检测采用自适应阈值法来二值化变化点和非变化点,变化分类基于颜色共生特征向量,采用贝叶斯规则进行决策,前景对象的提取融合了时间差分法和减背景法.针对复杂场景中背景的"渐变"和"突变"情况,提出了不同的背景更新策略.实验表明,该方法在包含有摇动的树枝,或者灯的开关等复杂背景中能准确地提取运动目标,因此可用在医学步态分析的研究中. 相似文献
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针对医学步态分析中的运动目标检测问题,提出了基于最小错误率的贝叶斯决策规则的方法.该方法由变化检测、变化分类、前景目标提取和背景更新四部分组成.变化检测采用自适应阈值法检测二值化变化点和非变化点.变化分类基于颜色共生特征向量,采用贝叶斯规则进行决策,前景对象的提取融合了时间差分法和减背景法.针对复杂场景中背景的"渐变"和"突变"情况,提出了不同的背景更新策略.实验表明,该方法能将包含有摇动的树枝或者灯的开关等复杂背景中运动目标准确地提取,可用在医学步态分析的研究中. 相似文献
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步态分类在人体运动能量消耗评估等应用中具有重要意义,提高分类精度和降低对统计特征的依赖是步态分类的研究热点。采用传统的步态分类方法提取的步态特征用于细分化步态时不能得到较好的效果。考虑到步态的连续性和不同轴之间信号的相关性,本文提出了基于CLSTM的步态分类方法:采用卷积神经网络(CNN)操作,通过计算多轴步态数据提取步态特征;基于长短期记忆(LSTM)构建步态时间序列模型,学习步态特征图时间维度上的长期依赖性。基于USC-HAD数据集的实验结果表明,用此方法提取了步态序列特征,很好地利用了步态时间序列特点,提升了11种步态的分类精度。 相似文献
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针对传统的自底向上的显著性检测模型突出背景、前景区域不均匀以及显著目标位于图像边缘致使检测效果差等问题,提出了一种基于多层图和紧凑性的显著性检测模型。首先,将图像过分割为超像素,在超像素基础上结合图像块层和聚类层构建多层图模型,能够有效检测不同尺度的图像并获得均匀的显著区域。然后,基于紧凑性假设建立紧凑性模型,并采用元胞自动机优化。根据超像素的紧凑性筛选出可靠的前景种子点和背景种子点,基于多层图模型利用流行排序算法分别计算基于前景种子点和背景种子点的排序分数,从目标和背景的角度结合两种排序分数得到显著图。最后,对显著图进行滤波获得光滑的前景和背景区域,得到最终显著图。在常用的数据集MSRA-1000和ECSSD上与9种流行算法进行比较,实验结果表明该算法具有较高的准确率和召回率。 相似文献
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目的 利用基于属性选择的贝叶斯网络对缺失的临床数据集进行分类预测.方法 首先为每个属性添加一个二元变量指示各属性丢失情况;接着使用基于包装法的遗传因子搜索法对原始的有缺失的临床数据集进行属性选择:最后应用贝叶斯网络对以上优化属性集进行分类并检验分类效果.结果 该方法不仅考虑到了丢失的临床信息的价值.也除去了无关和冗余的属性.结论 本文提出的方法,分类效果优于直接使用贝叶斯网络模型. 相似文献
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目的:利用中国数字人工程获得的冷冻人体切片数据,建立虚拟人技术仿真平台,为人体骨肌系统生物力学及相关应用研究提供基础计算平台服务。方法:首先通过课题组自行开发的医学图像处理软件CryoSegmentation,对中国数字人工程男一号的冷冻切片图像序列进行配准、特征点和轮廓线的提取等处理。然后利用逆向工程软件Imageware建立虚拟人体的面模型。其次基于Hamilton动力学理论,建立虚拟人体运动学与动力学方程;并结合人体运动捕捉系统,在Mircrosoft Visual Studio2005环境下,开发虚拟人运动学与动力学计算模块。最后,根据人体骨骼的材料属性和力学特性,建立了虚拟人全身骨骼系统的有限元模型。结果:通过对一名志愿者进行的步态实验,获得了该志愿者的计算机虚拟模型,捕捉到步态实验中人体各关节的运动学参数,通过动力学模块的计算,得到了步态中人体各关节的受力情况。通过对步态中股骨的有限元计算,得到了志愿者行走中股骨所受的应力云图。结论:基于数字人技术的人体骨肌系统生物力学计算平台能够对人体进行生物力学仿真和计算,从而得到人体运动过程中准确的力学参数,为临床骨科医学、人体工效学、体育与艺术运动、军事医学等相关学科的应用研究提供参考依据。 相似文献
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目的探讨病例分型与住院病例费用结构的变化规律。方法从“军字一号”医院信息系统中提取2006年-2008年出院病人86,281份数据,运用病例分型软件对86,281例出院病人医疗费用结构进行统计对比分析。结果四种病例分型的医疗费用排序:D型〉C型〉A型〉B型。结论病例分型法分析医疗费用结构,有利于客观地评价住院病人医疗费用结构的合理性,为医院管理者评价和控制目前不断增长的医疗费用提供了新的管理模式。 相似文献
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Jung-Ah Lee Sang-Hyun Cho Young-Jae Lee Heui-Kyung Yang Jeong-Whan Lee 《Journal of medical systems》2010,34(5):959-966
A portable and wireless activity monitoring system was developed for the estimation of temporal gait parameters. The new system
was built using three-axis accelerometers to automatically detect walking steps with various walking speeds. The accuracy
of walking step-peak detection algorithm was assessed by using a running machine with variable speeds. To assess the consistency
of gait parameter analysis system, estimated parameters, such as heel-contact and toe-off time based on accelerometers and
footswitches were compared for consecutive 20 steps from 19 individual healthy subjects. Accelerometers and footswitches had
high consistency in the temporal gait parameters. The stance, swing, single-limb support, and double-limb support time of
gait cycle revealed ICCs values of 0.95, 0.93, 0.86, and 0.75 on the right and 0.96, 0.86, 0.93, 0.84 on the left, respectively.
And the walking step-peak detection accuracy was 99.15% (±0.007) for the proposed method compared to 87.48% (±0.033) for a
pedometer. Therefore, the proposed activity monitoring system proved to be a reliable and useful tool for identification of
temporal gait parameters and walking pattern classification. 相似文献
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With the growing use of minimally invasive surgical procedures, endoscopic video archives are growing at a rapid pace. Efficient access to relevant content in such huge multimedia archives require compact and discriminative visual features for indexing and matching. In this paper, we present an effective method to represent images using salient convolutional features. Convolutional kernels from the first layer of a pre-trained convolutional neural network (CNN) are analyzed and clustered into multiple distinct groups, based on their sensitivity to colors and textures. Dominant features detected by each cluster are collected into a single, layout-preserving feature map using a spatial maximal activator pooling (SMAP) approach. A moving window based structured pooling method then captures spatial layout features and global shape information from the aggregated feature map to populate feature histograms. Finally, individual histograms for each cluster are combined into a single comprehensive feature histogram. Clustering convolutional feature space allow extraction of color and texture features of varying strengths. Further, the SMAP approach enable us to select dominant discriminative features. The proposed features are compact and capable of conveniently outperforming several existing features extraction approaches in retrieval and classification tasks on endoscopy images dataset. 相似文献
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Zohreh HosseinKhani Mohsen Hajabdollahi Nader Karimi Reza Soroushmehr Shahram Shirani Kayvan Najarian Shadrokh Samavi 《Journal of medical systems》2018,42(11):216
Noise is an important factor that degrades the quality of medical images. Impulse noise is a common noise caused by malfunctioning of sensor elements or errors in the transmission of images. In medical images due to presence of white foreground and black background, many pixels have intensities similar to impulse noise and hence the distinction between noisy and regular pixels is difficult. Therefore, it is important to design a method to accurately remove this type of noise. In addition to the accuracy, the complexity of the method is very important in terms of hardware implementation. In this paper a low complexity de-noising method is proposed that distinguishes between noisy and non-noisy pixels and removes the noise by local analysis of the image blocks. All steps are designed to have low hardware complexity. Simulation results show that in the case of magnetic resonance images, the proposed method removes impulse noise with an acceptable accuracy. 相似文献
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The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG. 相似文献
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医学模式的转变要求医生的服务观念需从"以疾病为中心"转变为"以病人为中心".医务工作者除了具备精湛的医术外,还需具有较高的人文素养和高尚的道德品质.根据当前时代背景和医学生医德养成的特点,从医德教育的核心内容、根本宗旨、基本方式、价值取向等方面进行论述,以探讨儒家思想对医学生医德教育的现实指导意义和教育价值. 相似文献