一种基于特征优化算法的磁共振脑组织分割方法

结合独立分量分析与支持向量机,提出一种基于特征优化算法的磁共振脑组织分割方法。首先,从图像中提取出灰度和纹理特征构成原始特征集;然后,利用独立分量分析技术对所提取的原始图像特征进行优化处理,提取其中的独立分量构成特征子集;最后,把训练样本与待分类样本都映射到特征子集所张成的独立空间中,利用特征子集对支持向量机分类器进行训练并对脑组织进行分类。实验结果表明,采用本研究的分割方法可以获得比其他相关方法更好的脑组织分割结果。     

基于能量特征和最小二乘支持向量机的自动睡眠分期方法

睡眠分期是研究睡眠及相关疾病的基础,是完成睡眠质量评估的前提。为实现有效睡眠自动分期,本文提出将能量特征和最小二乘支持向量机(LS-SVM)相结合的方法。先利用FIR带通滤波器提取Pz-Oz导睡眠脑电信号的特征波,获得能量特征,并与小波包变换方法相比较;然后用LS-SVM分类器进行模式识别,最终实现睡眠自动分期。实验表明,本文所提出的基于能量特征和LS-SVM的自动睡眠分期方法简单、有效,平均正确率达88.89%,具有很好的应用前景。     

基于形状纹理特征的食管癌和肝包虫病图像分类

目的:探讨用K最近邻(KNN)分类算法对食管癌X射线图像和肝包虫CT图像的Hu不变矩形状特征和小波变换纹理特征进行分类研究。方法:利用Hu不变矩算法和小波变换算法对食管癌X射线图像和肝包虫CT图像提取特征,用KNN分类器对特征值进行分类以验证所提取特征的分类能力。结果:对于食管癌X射线图像使用Hu不变矩算法提取形状特征具有较好的分类性能,对于肝包虫CT图像使用小波变换算法提取纹理特征具有较好的分类性能。结论:Hu不变矩形状特征结合KNN分类器的研究方法为新疆哈萨克族食管癌的分型提供一定的依据,小波变换纹理特征结合KNN分类器的研究方法为地方性肝包虫病的分型提供一定的依据,同时为计算机辅助诊断系统的研发奠定基础。     

基于支持向量机及特征选择的单通道脑电波睡眠分期研究

睡眠脑电是研究睡眠障碍及相关疾病的重要客观指标。人工解析脑电方法耗时且易受主观因素影响,而已有的自动睡眠分期算法则较为复杂且正确率较低。本文提出基于支持向量机(SVM)及特征选择的单通道脑电睡眠分期方法。从单通道脑电波信号中提取了38个特征值。在此基础上,通过将特征选择方法 F-Score拓展到多分类,增加淘汰因子,为SVM分类器选择合适的输入特征向量组。文章采用标准的开源数据,对比实验了无特征选择、标准的F-Score特征选择以及带有淘汰制的F-Score特征选择三种方法。实验结果表明,本文提出的方法能够有效提高分期正确率,减少计算时间。     

脑梗塞患者MR图像纹理特征的研究

目的：本文基于灰度共生矩阵和游程长矩阵方法研究脑梗塞患者MR图像的纹理特征,目的在于揭示脑梗塞患者与健康对照组MR图像纹理是否存在显著性差异,从而借助这一微观改变实现脑梗塞患者的早期诊断。方法：提取患病组和健康对照组纹理特征参量,利用fisher系数进行有效纹理特征参量的筛选,构建分类器。结果：LDA分类器识别率为88．31％。说明脑梗塞患者和对照组MR图像纹理特征存在差异。结论：利用基于统计学的纹理分析方法可以有效的揭示脑梗塞患者脑组织纹理的细微改变,进而实现对脑梗塞疾病的早期诊断。     

新疆高发病食管癌图像的特征提取及分类

目的结合灰度共生矩阵和小波变换的纹理分析方法提取新疆哈萨克族高发病食管癌X射线钡剂造影图像的特征,旨在为放射科医生的诊断决策提供具有实际参考价值的辅助信息,提高食管癌诊断的准确率和效率。方法选取2种中晚期食管癌——蕈伞型和缩窄型,以及正常食管图像各100张,利用基于灰度共生矩阵的纹理特征提取方法分别提取食管癌X射线图像的角二阶矩、熵、惯性矩、逆差矩及相关性的方差作为纹理特征,同时使用小波变换对食管癌X射线图像进行二层小波分解,获取其高频子图,并提取高频子图的能量特征作为纹理特征。然后,使用C4.5决策树算法构造一个分类器,对正常食管和中晚期食管癌图像进行分类研究。结果共计提取11维特征,利用单一特征算法进行分类,灰度共生矩阵法分类准确率为64.66%,小波变换法分类准确率为77%。而综合的灰度共生矩阵和小波变换法的分类准确率为81.67%,更适用于正常食管和中晚期食管癌的分类。结论本研究将灰度共生矩阵、小波变换算法与决策树C4.5相结合,对正常食管与蕈伞型和缩窄型食管癌进行特征提取及分析,结果表明本算法分类准确率较高,为开发食管癌的计算机辅助诊断系统奠定了基础。     

基于数据挖掘的新疆高发肝包虫病的分型研究

目的探讨数据挖掘技术在新疆肝包虫病分型中的应用。方法提取肝包虫病CT图像的灰度-梯度共生矩阵(GGCM)和灰度共生矩阵(GLCM)特征,应用主成分分析法对各纹理特征及混合特征分别进行降维,采用支持向量机(SVM)分类器、决策树C4.5分类器、Logistic回归分类器对降维后的特征进行分类,最后对各分类模型进行受试者工作特性(ROC)曲线分析及参数评估。结果 SVM分类器对不同纹理特征下3种肝脏CT图像(单囊型、多囊型肝包虫病和正常肝脏)分类效果都明显优于决策树C4.5分类器和Logistic回归分类器。综合特征分类结果要明显优于单一特征分类结果;GGCM特征对综合分类结果的分类贡献率要高于GLCM特征。结论将SVM分类器应用于新疆肝包虫病CT图像的分型中具有一定分类优势,为肝包虫病影像学诊断提供了一定的依据,也为后期新疆肝包虫病计算机辅助诊断系统的研发奠定基础。     

基于肺部PET/CT图像不同纹理特征的K最近邻分类器

目的对PET/CT图像高维纹理参数进行降维,基于不同纹理参数建立肺结节良恶性的K最近邻(K-nearest neighbor,KNN)分类器,探究最佳建模方法,提高分类的准确率。方法采用回顾性研究的方式,收集52例首都医科大学宣武医院核医学科肺结节患者的PET/CT图像,对图像的感兴趣区域基于Contourlet变换提取灰度共生矩阵的纹理参数。对肺结节PET/CT图像的纹理参数首先采用单因素分析的方法,根据ROC曲线下面积筛选纹理参数,再对其进行主成分分析提取主要成分。基于主成分、根据ROC曲线筛选的纹理及原始纹理分别采用K最近邻分类算法建立肺结节良恶性的分类器,通过正确率、灵敏度、特异度、阳性预测值(positive predictive value,PPV)、阴性预测值(negative predictive value,NPV)、ROC曲线下面积(area under curve,AUC)这些指标评价分类效果。结果 PET/CT图像共提取1344个原始纹理参数,经单因素分析后筛选出89个纹理参数,对筛选后的纹理共提取11个主成分。基于主成分、筛选纹理、原始纹理的分类模型正确率分别为0.614、0.579、0.263;AUC分别为0.645、0.610、0.515。结论在主成分纹理、单因素分析筛选的纹理、原始纹理中,基于主成分纹理建立K最近邻分类器的效果最好。     

基于ABC-SVM的运动想象脑电信号模式分类

为了提高运动想象脑电信号分类的准确率,针对传统支持向量机（SVM）分类方法在脑电信号处理中存在寻优繁 琐、工作量大和分类正确率低等问题,本研究提出一种基于人工蜂群（ABC）算法优化SVM的分类识别方法。首先利用正 则化共空间模式对脑电信号进行特征提取,然后利用ABC算法优化SVM的惩罚因子和核参数,最后利用提取的右手和 右脚两类脑电信号样本特征对优化后的SVM进行训练和分类测试。实验结果表明ABC-SVM分类器提高了脑电信号分 类的准确率,比传统的SVM分类器准确率高出2.5%,证明该算法的可行性和较高准确性。     

一种基于改进人工蜂群算法优化支持向量机的睡眠呼吸暂停检测方法

目的 睡眠呼吸暂停综合征(sleep apnea syndrome, SAS)是由于睡眠时上气道通气不畅或堵塞引起的呼吸暂停或低通气,严重影响人类健康和生活。目前的检测方法是多导睡眠仪,检测过程较为复杂,影响患者正常睡眠。为此本文提出了一种针对血氧饱和度信号的引入交叉变异的全局混沌人工蜂群(cross global chaos artificial bee colony, CGCABC)算法优化支持向量机(support vector machine, SVM)的SAS检测方法。方法 从数据集ISRUC-SLEEP中提取25名SAS患者整晚8 h的脉搏血氧饱和度数据,经预处理后对每段数据计算5种非线性特征,包括近似熵、模糊熵、信息熵、排列熵和样本熵。比较发病片段信号特征和未发病片段信号特征之间的差异,使用CGCABC算法优化的SVM模型进行分类检测,并与人工蜂群(artificial bee colony, ABC)算法、粒子群(particle swarm optimization, PSO)算法、麻雀搜索(sparrow search, SS)算法优化SVM模型的检测结果进行对比。...     

Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability

This paper proposes a congestive heart failure (CHF) recognition method that includes features calculated from the bispectrum of heart rate variability (HRV) diagrams and a genetic algorithm (GA) for feature selection. The roles of the bispectrum-related features and the GA feature selector are investigated. Features calculated from the subband regions of the HRV bispectrum are added into a feature set containing only regular time-domain and frequency-domain features. A support vector machine (SVM) is employed as the classifier. A feature selector based on genetic algorithm proceeds to select the most effective features for the classifier. The results confirm the effectiveness of including bispectrum-related features for promoting the discrimination power of the classifier. When compared with the other two methods in the literature, the proposed method (without GA) outperforms both of them with a high accuracy of 96.38%. More than 3.14% surpluses in accuracies are observed. The application of GA as a feature selector further elevates the recognition accuracy from 96.38% to 98.79%. When compared to the Isler and Kuntalp's impressive results recently published in the literature that also uses GA for feature selection, the proposed method (with GA) outperforms them with more than 2.4% surpass in the recognition accuracy. These results confirm the significance of recruiting bispectrum-related features in a CHF classification system. Moreover, the application of GA as feature selector can further improve the performance of the classifier.     

基因位点预测的一种特征选择优化算法

目的剪接位点是真核细胞生物基因序列中外显子和内含子的相邻区域,如果能准确预测基因序列中的剪接位点,就能将基因中的表达区域和非表达区域分开.方法从机器学习的角度出发,提出了一种有效的特征选择算法用于剪接位点的建模和预测.该算法首先将初始链模型中每一对父子节点作为特征量提取,然后通过遗传算法和最大后验分类器进行特征选择.结果及结论对剪接位点数据的预测结果显示,这种新算法能够有效地优化链模型的结构,提高对剪接位点的预测能力.同时,经过优化的模型也有助于了解真核细胞中基因转录和表达的过程.     

Feature selection and classifier performance in computer-aided diagnosis: the effect of finite sample size

In computer-aided diagnosis (CAD), a frequently used approach for distinguishing normal and abnormal cases is first to extract potentially useful features for the classification task. Effective features are then selected from this entire pool of available features. Finally, a classifier is designed using the selected features. In this study, we investigated the effect of finite sample size on classification accuracy when classifier design involves stepwise feature selection in linear discriminant analysis, which is the most commonly used feature selection algorithm for linear classifiers. The feature selection and the classifier coefficient estimation steps were considered to be cascading stages in the classifier design process. We compared the performance of the classifier when feature selection was performed on the design samples alone and on the entire set of available samples, which consisted of design and test samples. The area Az under the receiver operating characteristic curve was used as our performance measure. After linear classifier coefficient estimation using the design samples, we studied the hold-out and resubstitution performance estimates. The two classes were assumed to have multidimensional Gaussian distributions, with a large number of features available for feature selection. We investigated the dependence of feature selection performance on the covariance matrices and means for the two classes, and examined the effects of sample size, number of available features, and parameters of stepwise feature selection on classifier bias. Our results indicated that the resubstitution estimate was always optimistically biased, except in cases where the parameters of stepwise feature selection were chosen such that too few features were selected by the stepwise procedure. When feature selection was performed using only the design samples, the hold-out estimate was always pessimistically biased. When feature selection was performed using the entire finite sample space, the hold-out estimates could be pessimistically or optimistically biased, depending on the number of features available for selection, the number of available samples, and their statistical distribution. For our simulation conditions, these estimates were always pessimistically (conservatively) biased if the ratio of the total number of available samples per class to the number of available features was greater than five.     

Comparison of feature selection and classification methods for a brain–computer interface driven by non-motor imagery

The aim of this study was to compare methods for feature extraction and classification of EEG signals for a brain–computer interface (BCI) driven by auditory and spatial navigation imagery. Features were extracted using autoregressive modeling and optimized discrete wavelet transform. The features were selected with exhaustive search, from the combination of features of two and three channels, and with a discriminative measure (r ²). Moreover, Bayesian classifier and support vector machine (SVM) with Gaussian kernel were compared. The results showed that the two classifiers provided similar classification accuracy. Conversely, the exhaustive search of the optimal combination of features from two and three channels significantly improved performance with respect to using r ² for channel selection. With features optimally extracted from three channels with optimized scaling filter in the discrete wavelet transform, the classification accuracy was on average 72.2%. Thus, the choice of features had greater impact on performance than the choice of the classifier for discrimination between the two non-motor imagery tasks investigated. The results are relevant for the choice of the translation algorithm for an on-line BCI system based on non-motor imagery.     

基于自步学习的多经验核映射集成分类器在乳腺癌超声计算机辅助诊断上的应用

特征表达和分类器的性能是决定计算机辅助诊断(CAD)系统性能的重要因素。为了提升基于超声成像的乳腺癌CAD系统的性能,本文提出了一种基于自步学习(SPL)的多经验核映射(MEKM)排他性正则化机(ERM)集成分类器算法,能同时提升特征表达和分类器模型的性能。该算法首先通过MEKM映射得到多组特征,以增强特征表达能力,并嵌入到ERM作为多个支持向量机的核变换;然后采用SPL策略自适应地选择样本,由易到难地逐步训练ERM集成分类器模型,从而提升分类器的性能。该算法分别在乳腺癌B型超声数据库和弹性超声数据库上进行了验证,结果显示B型超声的分类准确率、敏感度和特异性分别为(86.36±6.45)%、(88.15±7.12)%和(84.52±9.38)%,而弹性超声的分类准确率、敏感度和特异性分别为(85.97±3.75)%、(85.93±6.09)%和(86.03±5.88)%。实验结果表明,本文所提出算法能有效提升乳腺超声CAD的性能,具有投入实用的潜能。     

A novel real-time patient-specific seizure diagnosis algorithm based on analysis of EEG and ECG signals using spectral and spatial features and improved particle swarm optimization classifier

This paper proposes a novel real-time patient-specific seizure diagnosis algorithm based on analysis of electroencephalogram (EEG) and electrocardiogram (ECG) signals to detect seizure onset. In this algorithm, spectral and spatial features are selected from seizure and non-seizure EEG signals by Gabor functions and principal component analysis (PCA). Furthermore, four features based on heart rate acceleration are extracted from ECG signals to form feature vector. Then a neural network classifier based on improved particle swarm optimization (IPSO) learning algorithm is developed to determine an optimal nonlinear decision boundary. This classifier allows to adjust the parameters of the neural network classifier, efficiently. This algorithm can automatically detect the presence of seizures with minimum delay which is an important factor from a clinical viewpoint. The performance of the proposed algorithm is evaluated on a dataset consisting of 154 h records and 633 seizures from 12 patients. The results indicate that the algorithm can recognize the seizures with the smallest latency and higher good detection rate (GDR) than other presented algorithms in the literature.     

Evolutionary weighting of image features for diagnosing of CNS tumors

This paper concerns an application of evolutionary feature weighting for diagnosis support in neuropathology. The original data in the classification task are the microscopic images of ten classes of central nervous system (CNS) neuroepithelial tumors. These images are segmented and described by the features characterizing regions resulting from the segmentation process. The final features are in part irrelevant. Thus, we employ an evolutionary algorithm to reduce the number of irrelevant attributes, using the predictive accuracy of a classifier ('wrapper' approach) as an individual's fitness measure. The novelty of our approach consists in the application of evolutionary algorithm for feature weighting, not only for feature selection. The weights obtained give quantitative information about the relative importance of the features. The results of computational experiments show a significant improvement of predictive accuracy of the evolutionarily found feature sets with respect to the original feature set.     

Identification of signatures in biomedical spectra using domain knowledge

OBJECTIVE: Demonstrate that incorporating domain knowledge into feature selection methods helps identify interpretable features with predictive capability comparable to a state-of-the-art classifier. METHODS: Two feature selection methods, one using a genetic algorithm (GA) the other a L(1)-norm support vector machine (SVM), were investigated on three real-world biomedical magnetic resonance (MR) spectral datasets of increasing difficulty. Consensus sets of the feature sets obtained by the two methods were also assessed. RESULTS AND CONCLUSIONS: Features identified independently by the two methods and by their consensus, determine class-discriminatory groups or individual features, whose predictive power compares favorably with that of a state-of-the-art classifier. Furthermore, the identified feature signatures form stable groupings at definite spectral positions, hence are readily interpretable. This is a useful and important practical result for generating hypothesis for the domain expert.     

Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms

Recent advances in neuroimaging demonstrate the potential of functional near-infrared spectroscopy (fNIRS) for use in brain–computer interfaces (BCIs). fNIRS uses light in the near-infrared range to measure brain surface haemoglobin concentrations and thus determine human neural activity. Our primary goal in this study is to analyse brain haemodynamic responses for application in a BCI. Specifically, we develop an efficient signal processing algorithm to extract important mental-task-relevant neural features and obtain the best possible classification performance. We recorded brain haemodynamic responses due to frontal cortex brain activity from nine subjects using a 19-channel fNIRS system. Our algorithm is based on continuous wavelet transforms (CWTs) for multi-scale decomposition and a soft thresholding algorithm for de-noising. We adopted three machine learning algorithms and compared their performance. Good performance can be achieved by using the de-noised wavelet coefficients as input features for the classifier. Moreover, the classifier performance varied depending on the type of mother wavelet used for wavelet decomposition. Our quantitative results showed that CWTs can be used efficiently to extract important brain haemodynamic features at multiple frequencies if an appropriate mother wavelet function is chosen. The best classification results were obtained by a specific combination of input feature type and classifier.