模糊聚类支持向量机在步态分类中的应用

背景：对于患有神经系统或骨骼肌肉系统疾病的患者,分析步态数据可以评定康复程度,制定治疗方案。如何有效地分类小样本步态数据成为重要的研究课题。目的：用改进的支持向量机算法对小样本步态数据进行分类,准确诊断疾病。方法：建立加入模糊C均值聚类的支持向量机算法,选用Gait Dynamics in Neuro-Degenerative Disease Data Base40～59岁年龄段的6组数据,共720个样本数据,采用左摆间隔和左支撑间隔两维参数对步态数据建模。数据归一化后,通过模糊C均值聚类对数据进行预处理;然后用支持向量机对数据进行分类。采用不同核函数的支持向量机算法验证分类能力。结果与结论：实验结果表明,利用改进的支持向量机算法,可以有效地对信号进行分类,有助于疾病的诊断和治疗方案的制定。     

一种改进的模糊核聚类算法

针对传统模糊核聚类算法当数据类差别很大时,小数据类被误分或被大数据类吞并的缺陷,提出了一种改进的模糊核聚类算法;通过定义一个新的目标函数,为每一个类分配了一个动态权值,可改善聚类效果.文中给出的MRI图像分割算例表明了该算法的有效性.     

点对称距离模糊C均值聚类算法在脑部MRI图像分割中的应用

背景:在传统的图像分割方法中,模糊C均值聚类算法应用十分广泛。目的:将改进的模糊C均值聚类算法应用到MRI图像的分割中,提高MRI图像分割的准确度。方法:针对传统的基于Minkowski距离的模糊C均值聚类算法,提出了基于点对称距离的模糊C均值聚类算法,并将其运用到了脑部MRI图像分割中。结果与结论:实验结果表明,与模糊C均值聚类算法相比,点对称距离的模糊C均值聚类算法有明显的优势。     

点对称距离模糊C均值聚类算法在脑部MRI图像分割中的应用

背景：在传统的图像分割方法中,模糊C均值聚类算法应用十分广泛。目的：将改进的模糊C均值聚类算法应用到MRI图像的分割中,提高MRI图像分割的准确度。方法：针对传统的基于Minkowski距离的模糊C均值聚类算法,提出了基于点对称距离的模糊C均值聚类算法,并将其运用到了脑部MRI图像分割中。结果与结论：实验结果表明,与模糊C均值聚类算法相比,点对称距离的模糊C均值聚类算法有明显的优势。     

CT三维最小类内散度多分类支持向量机在肺结节识别中的应用

目的分析CT三维最小类内散度多分类支持向量机(MC-SVM)对肺结节的识别能力及优点。方法选择2012年1月至2014年1月确诊的肺结节病患者50例,根据基于三维矩阵模式的感兴趣体(VOI)的构成,分为结节样和非结节样;采用自动提取算法提取感兴趣区(ROI),分为结节ROI和非结节ROI;采用受试者工作特征(ROC)曲线比较大规模训练人工神经网络(MTANN)、基于矩阵模式的模糊最小二乘SVM(matFLSSVM)、三维矩阵模式MC-SVM和三维最小类内散度MC-SVM的识别精度,同时比较各种方法在不同截断点时的真阳性率和假阳性率。结果三维最小类内散度MC-SVM的识别精度、真阳性率均高于其他算法,而假阳性率低于其他算法(P0.05)。结论三维最小类内散度MC-SVM对肺结节的识别精度较高,值得临床推广应用。     

组合核支持向量机在放电模式识别中的优化策略

传统的单一核函数SVM无法实现局部放电中的多特征空间向量的映射分类,且目前SVM大都采用不同尺度的径向基函数作为核函数,核的调整空间较为有限,无法针对不同特征空间的放电参数达到普适的效果。针对上述问题,本文提出一种基于多分组特征的组合核多分类SVM的局部放电识别方法。该方法首先完成多类特征空间在不同类型SVM核函数中的映射分类,再采用骨干粒子群(BBPSO)优化方法选取最佳核参数,并求解核函数权值系数,最终形成最优核函数组合分类模型。实验结果表明,该方法对多个特征空间数据具有普适性,且融合效果理想,分类精度高于误差反向传播神经网络(BPNN)和SVM识别方法。     

模糊K-均值聚类算法及其在磁共振颅脑图像分割中的应用研究

目的　介绍一种动态模糊聚类算法并利用该算法对磁共振图像进行分割研究。方法　首先对磁共振颅脑图像进行预处理去掉颅骨和肌肉等非脑组织,只保留大脑组织,然后利用模糊Ｋ－ 均值聚类算法计算脑白质、脑灰质和脑脊液的模糊类属函数。结果　模糊Ｋ－ 均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、白质和脑脊液。结论　利用模糊Ｋ－ 均值聚类算法分割磁共振颅脑图像能获得较好的分割效果。     

模糊K—均聚类算法及其在磁共振颅脑图像分割中的应用研究

目的 介绍一种动态模糊聚类算法并和该算法对磁共振图像进行分割研究。方法 首先对磁共振颅脑图像进行预处理去掉颅骨和肌肉等非脑组织,只保留大脑组织,然后利用模糊Ｋ－均值聚类算法计算脑白质、脑灰质和脑脊液的模糊类属函数。结果 模糊Ｋ－均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、白质和脑脊液。结论 利用模糊Ｋ－均值聚类算法分割磁共振颅脑图像能获得较好的分割效果。     

基于微量元素支持向量机在鼻咽癌模型预测中的应用

背景:支持向量机目前已经在文本分类、手写识别、图像分类、生物信息学等诸多领域被成功应用.目的:采用智能算法,将支持向量机算法与微量元素数据结合对鼻咽癌患者建模,以提高鼻咽癌识别正确率.方法:基于微量元素数据,利用支持向量机对鼻咽癌患者、正常人、其他疾病患者样本建立分类模型.样品取自观察对象未染发头枕部紧贴头皮3 cm的头发.对样本进行的临床微量元素检测项目为6种元素锌、铜、铁、锰、镉、镍,加上年龄和性别共8项.采用高斯径向基函数为核函数、调节核函数参数C及σ以建立最佳支持向量机模型.结果与结论:采用十折交叉验证法得到模型的识别率分别为81.71%和66.47%.结果表明,基于微量元素的支持向量机法建立的鼻咽癌分类模型能较好的把鼻咽癌样本从正常人、各种疾病患者样本中区分出来.     

模糊支持向量分类判别睡眠呼吸暂停综合征

背景:目前,睡眠呼吸暂停综合征的诊断主要依赖多导睡眠分析仪,该测量方法不但操作复杂、费用昂贵、分析耗时,而且在一定程度上影响患者的睡眠状况.目的:分析心率变异性与睡眠呼吸暂停综合征的关系,提出一种简便准确的睡眠呼吸暂停综合征的检测算法.方法:对38名健康者和28例不同程度睡眠呼吸暂停综合征患者的心率数据,采用去趋势波动分析法和自回归模型谱分析法,分析心率变异性与睡眠时相的相关性,并选取患者的性别、年龄以及心率变异性在各个睡眠阶段的标度指数及低频/高频比例作为睡眠呼吸暂停综合征初筛的特征参数,应用模糊支持向量机对睡眠呼吸暂停综合征阳性和阴性进行分类判别.结果与结论:实验结果表明,模糊支持向量机的分类正确率达到93.94%.与现有睡眠呼吸暂停综合征的诊断方法相比,该方法测量简单方便,具有较高的诊断准确率.     

Hyperspectral data classification using geostatistics and support vector machines

Hyperspectral imagery combined with spatial features holds promise for improved remote sensing classification. In this letter, we propose a method for classification of hyperspectral data based on the incorporation of spatial arrangement of pixel's values. We use the semivariogram to measure the spatial correlation which is then combined with spectral features within the stacked kernel support vector machine framework. The proposed method is compared with a classifier based on first-order statistics. The overall classification accuracy is tested for the AVIRIS Indian Pines benchmark dataset. Error matrices are used to estimate individual class accuracy. Statistical significance of the accuracy estimates is assessed based on the kappa coefficient and z-statistics at the 95% confidence level. Empirical results show that the proposed approach gives better performance than the method based on first-order statistics.     

Automatic classification of gait patterns in children with cerebral palsy using fuzzy clustering method

BackgroundSubjective classification of gait pattern in children with cerebral palsy depends on the assessor's experience, while mathematical methods produce virtual groups with no clinical interpretation.MethodsIn a retrospective study, gait data from 66 children (132 limbs) with a mean age of 9.6 (SD 3.7) years with cerebral palsy and no history of surgery or botulinum toxin injection were reviewed. The gait pattern of each limb was classified in four groups according to Rodda using three methods: 1) a team of experts subjectively assigning a gait pattern, 2) using the plantarflexor-knee extension couple index introduced by Sangeux et al., and 3) employing a fuzzy algorithm to translate the experiences of experts into objective rules and execute a clustering tool. To define fuzzy repeated-measures, 75% of the members in each group were used, and the remaining were used for validation. Eight parameters were objectively extracted from kinematic data for each group and compared using repeated measure ANOVA and post-hoc analysis was performed. Finally, the results of the clustering of the latter two methods were compared to the subjective method.FindingsThe plantarflexor-knee extension couple index achieved 86% accuracy while the fuzzy system yielded a 98% accuracy. The most substantial errors occurred between jump and apparent in both methods.InterpretationThe presented method is a fast, reliable, and objective fuzzy clustering system to classify gait patterns in cerebral palsy, which produces clinically-relevant results. It can provide a universal common language for researchers.     

Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines

Background

This study aimed to investigate the optimal support vector machines (SVM)-based classifier of duchenne muscular dystrophy (DMD) magnetic resonance imaging (MRI) images.

Methods

T1-weighted (T1W) and T2-weighted (T2W) images of the 15 boys with DMD and 15 normal controls were obtained. Textural features of the images were extracted and wavelet decomposed, and then, principal features were selected. Scale transform was then performed for MRI images. Afterward, SVM-based classifiers of MRI images were analyzed based on the radical basis function and decomposition levels. The cost (C) parameter and kernel parameter \(\gamma \) were used for classification. Then, the optimal SVM-based classifier, expressed as \((C,\gamma \)), was identified by performance evaluation (sensitivity, specificity and accuracy).

Results

Eight of 12 textural features were selected as principal features (eigenvalues \(\lambda _{\mathrm{c}}\ge 1\)). The 16 SVM-based classifiers were obtained using combination of (C, \(\gamma \)), and those with lower C and \(\gamma \) values showed higher performances, especially classifier of \((C = 1,\gamma = 0.083)\,(p<0.05\)). The SVM-based classifiers of T1W images showed higher performance than T1W images at the same decomposition level. The T1W images in classifier of \((C = 1,\gamma = 0.083\)) at level 2 decomposition showed the highest performance of all, and its overall correct sensitivity, specificity, and accuracy reached 96.9, 97.3, and 97.1 %, respectively.

Conclusion

The T1W images in SVM-based classifier \((C =1, \gamma = 0.083)\) at level 2 decomposition showed the highest performance of all, demonstrating that it was the optimal classification for the diagnosis of DMD.

     

Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns

In functional brain mapping, pattern recognition methods allow detecting multivoxel patterns of brain activation which are informative with respect to a subject's perceptual or cognitive state. The sensitivity of these methods, however, is greatly reduced when the proportion of voxels that convey the discriminative information is small compared to the total number of measured voxels. To reduce this dimensionality problem, previous studies employed univariate voxel selection or region-of-interest-based strategies as a preceding step to the application of machine learning algorithms. Here we employ a strategy for classifying functional imaging data based on a multivariate feature selection algorithm, Recursive Feature Elimination (RFE) that uses the training algorithm (support vector machine) recursively to eliminate irrelevant voxels and estimate informative spatial patterns. Generalization performances on test data increases while features/voxels are pruned based on their discrimination ability. In this article we evaluate RFE in terms of sensitivity of discriminative maps (Receiver Operative Characteristic analysis) and generalization performances and compare it to previously used univariate voxel selection strategies based on activation and discrimination measures. Using simulated fMRI data, we show that the recursive approach is suitable for mapping discriminative patterns and that the combination of an initial univariate activation-based (F-test) reduction of voxels and multivariate recursive feature elimination produces the best results, especially when differences between conditions have a low contrast-to-noise ratio. Furthermore, we apply our method to high resolution (2 x 2 x 2 mm(3)) data from an auditory fMRI experiment in which subjects were stimulated with sounds from four different categories. With these real data, our recursive algorithm proves able to detect and accurately classify multivoxel spatial patterns, highlighting the role of the superior temporal gyrus in encoding the information of sound categories. In line with the simulation results, our method outperforms univariate statistical analysis and statistical learning without feature selection.     

Early prediction of unexplained syncope by support vector machines

The goal of the present study was to develop and evaluate a new method for the prediction of unexplained syncope occurrences. Diagnosis of syncope is currently based on the reproduction of symptoms in combination with hypotension and bradycardia induced by a 45 min 60-70 degrees head-upright tilt test (HUTT). The main drawback of this widely used test concerns its duration that reaches 55 min if the patient does not faint. Our method is a first step in the avoidance of the HUTT. An electrocardiogram and a transthoracic impedance waveform were recorded for 10 min of supine rest of a HUTT in 128 patients with a history of unexplained recurrent syncope. Seven indices were computed on the transthoracic impedance and its first derivative. The prediction quality of every subset of these variables, mixed with age and sex, has been tested by a support vector machine in a retrospective group of 64 patients (100% of sensitivity and 100% of specificity was reached). The best subset obtained has been evaluated prospectively in a group of 64 patients (94% of sensitivity and 79% of specificity was reached). These results compare very favorably with published results for other unexplained syncope detectors.     

Breast tumor classification using fuzzy clustering for breast elastography

Elastography is a new ultrasound imaging technique to provide the information about relative tissue stiffness. The elasticity information provided by this dynamic imaging method has proven to be helpful in distinguishing benign and malignant breast tumors. In previous studies for computer-aided diagnosis (CAD), the tumor contour was manually segmented and each pixel in the elastogram was classified into hard or soft tissue using the simple thresholding technique. In this paper, the tumor contour was automatically segmented by the level set method to provide more objective and reliable tumor contour for CAD. Moreover, the elasticity of each pixel inside each tumor was classified by the fuzzy c-means clustering technique to obtain a more precise diagnostic result. The test elastography database included 66 benign and 31 malignant biopsy-proven tumors. In the experiments, the accuracy, sensitivity, specificity and the area index Az under the receiver operating characteristic curve for the classification of solid breast masses were 83.5% (81/97), 83.9% (26/31), 83.3% (55/66) and 0.902 for the fuzzy c-means clustering method, respectively, and 59.8% (58/97), 96.8% (30/31), 42.4% (28/66) and 0.818 for the conventional thresholding method, respectively. The differences of accuracy, specificity and Az value were statistically significant (p < 0.05). We conclude that the proposed method has the potential to provide a CAD tool to help physicians to more reliably and objectively diagnose breast tumors using elastography.(E-mail: rfchang@csie.ntu.edu.tw)