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。     

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。     

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。     

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。     

基于小波的磁共振图像聚类分割

目的 利用直方图自适应确定人体不同部位MRI的聚类类别的数目和相应的初始聚类中心,实现模糊-c均值聚类算法(FCM)分割的自适应。方法 首先采用小波变换拟合直方图的平滑包络线,降低噪声对寻找包络线极值的影响;其次根据微积分的知识求出包络线极大值的个数,按照文中给出的法则对包络线的极大值进行筛选,确定直方图中峰值的个数;最后以直方图中峰值的个数为聚类类别数,以相应的峰值为初始聚类中心,对MRI进行FCM分割。结果 采用该方法对多幅腹部和脑部MR图像进行分割,均能有效地自适应确定聚类的个数。结论 本文方法能够有效、准确地确定不同MR图像的聚类类别的个数,实现FCM的自适应。     

改进模糊C-均值分割算法在多发性硬化症MR脑部图像中的应用

背景:脑部MR图像是一种无纹理的图像,未被噪声污染的脑部MR图像的灰度值具有分片为常数的特点.因此,在聚类过程中灰度值有趋向于在同一个分割区域中相对接近的性质.目的:寻找一个能够自动分割多发性硬化症病灶的模糊C-均值改进方法,为临床对于多发性硬化症的判断提供更方便的工具.方法:考虑到脑部MR图像相邻象素属于同一分类的概率相近的特性,在迭代过程中对8邻域数据集进行滤波以降低噪声对聚类精度的影响,提出了一种改进的模糊C-均值聚类算法.就是将模糊C-均值聚类算法迭代过程中得到的灰度值看作一个数据集,用每个象素邻域象素的灰度值修正该象素的模糊隶属度取值,从而达到利用空间信息抑制噪声的目的.结果与结论:选取了10个多发性硬化症患者的脑部MRI图像进行试验.通过对多发性硬化症患者MR T1脑部图像和T2液体衰减反转回复脑部图像的分割实验,结果显示该算法能够有效分割多发性硬化症病灶,与其他方法所做的多发性硬化症病灶分割相比,本算法更易于实现,运算时间短,同时结果与临床医生的勾画比较重叠率较高,对其临床辅助诊断具有重要作用.     

改进模糊C-均值分割算法在多发性硬化症MR脑部图像中的应用

背景：脑部MR图像是一种无纹理的图像,未被噪声污染的脑部MR图像的灰度值具有分片为常数的特点。因此,在聚类过程中灰度值有趋向于在同一个分割区域中相对接近的性质。目的：寻找一个能够自动分割多发性硬化症病灶的模糊C-均值改进方法,为临床对于多发性硬化症的判断提供更方便的工具。方法：考虑到脑部MR图像相邻象素属于同一分类的概率相近的特性,在迭代过程中对8邻域数据集进行滤波以降低噪声对聚类精度的影响,提出了一种改进的模糊C-均值聚类算法。就是将模糊C-均值聚类算法迭代过程中得到的灰度值看作一个数据集,用每个象素邻域象素的灰度值修正该象索的模糊隶属度取值,从而达到利用空间信息抑制噪声的目的。结果与结论：选取了10个多发性硬化症患者的脑部MRI图像进行试验。通过对多发性硬化症患者MRT1脑部图像和T2液体衰减反转回复脑部图像的分割实验,结果显示该算法能够有效分割多发性硬化症病灶,与其他方法所做的多发性硬化症病灶分割相比,本算法更易于实现,运算时间短,同时结果与临床医生的勾画比较重叠率较高,对其临床辅助诊断具有重要作用。     

运用模糊聚类方法分析中药微量元素含量与药效的关系

目的:不同中药中各种微量元素的含量数据离散,从含量数据上很难说明不同药物的药物相似性,故运用模糊分类方法研究药物微量元素含量与药物疗效的关系,验证其在药物功效的相似性及不同种类中药定量指标评价方面的作用。方法:实验于2005-10/2006-11在吉林省中医药管理局中药微量元素分析实验室完成。用原子吸收分光光度法检测合欢花、黄芪、石菖蒲、远志、山楂、银杏叶、人参、元肉、蒲黄、酸枣仁、苏木11种中药中钾、钠、钙、镁、铁、铬、镍、锰、铜、锌元素的含量,结合锌/铜比值、镁/钙比值,以数据极差法进行统计指标的标准化处理,以数量积法算出衡量被分类对象之间的相关系数,确定论域上的模糊矩阵。结果:相关系数r=0.50时,合欢花,石菖蒲,远志,人参,元肉,蒲黄,酸枣仁7种药物聚类相似;相关系数r=0.65时,合欢花,石菖蒲,元肉,蒲黄,酸枣仁5种药物聚类相似;r=0.75时,合欢花,石菖蒲,蒲黄3种药物聚类相似。研究还发现:药物中金属元素含量丰富,而且相关系数较大的药物,其药物疗效相似。结论:运用数学与化学计量学方法建立模糊聚类分析模型,可以把离散的数据标准化处理,确定论域上的模糊矩阵可以研究不同药物间的药效相似性,可以对不同种类的中药进行定量指标的明确评价。     

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)     

Lesion identification using unified segmentation-normalisation models and fuzzy clustering

In this paper, we propose a new automated procedure for lesion identification from single images based on the detection of outlier voxels. We demonstrate the utility of this procedure using artificial and real lesions. The scheme rests on two innovations: First, we augment the generative model used for combined segmentation and normalization of images, with an empirical prior for an atypical tissue class, which can be optimised iteratively. Second, we adopt a fuzzy clustering procedure to identify outlier voxels in normalised gray and white matter segments. These two advances suppress misclassification of voxels and restrict lesion identification to gray/white matter lesions respectively. Our analyses show a high sensitivity for detecting and delineating brain lesions with different sizes, locations, and textures. Our approach has important implications for the generation of lesion overlap maps of a given population and the assessment of lesion-deficit mappings. From a clinical perspective, our method should help to compute the total volume of lesion or to trace precisely lesion boundaries that might be pertinent for surgical or diagnostic purposes.     

基于GA与模糊连接的冠状动脉造影图像分割

目的 针对模糊连接图像分割方法在实现上的困难,提出了一种基于遗传算法的模糊连接分割方法.方法 首先把路径强度作为遗传算法的寻优条件,从而得到图像中各点到种子点间的最优路径,其强度即该点的模糊连接度.然后,以各点的模糊连接度作为其灰度值,形成目标和背景不交叉的新图,最后用阈值法把目标物体提取出来.结果 实验结果表明,将该方法应用于冠状动脉造影图像的血管分割,能准确快速地将血管从背景中分割出来.结论 该分割方法较好地满足了临床冠心病诊断的要求.     

Hierarchical rule-based monitoring and fuzzy logic control for neuromuscular block

Objective. The important task for anaesthetists is to provide an adequate degree of neuromuscular block during surgical operations, so that it should not be difficult to antagonize at the end of surgery. Therefore, this study examined the application of a simple technique (i.e., fuzzy logic) to an almost ideal muscle relaxant (i.e., rocuronium) at general anaesthesia in order to control the system more easily, efficiently, intelligently and safely during an operation. Methods. The characteristics of neuromuscular blockade induced by rocuronium were studied in 10 ASA I or II adult patients anaesthetized with inhalational (i.e., isoflurane) anaesthesia. A Datex Relaxograph was used to monitor neuromuscular block. And, ulnar nerve was stimulated supramaximally with repeated train-of-four via surface electrodes at the wrist. Initially a notebook personal computer was linked to a Datex Relaxograph to monitor electromyogram (EMG) signals which had been pruned by a three-level hierarchical structure of filters in order to design a controller for administering muscle relaxants. Furthermore, a four-level hierarchical fuzzy logic controller using the fuzzy logic and rule of thumb concept has been incorporated into the system. TheStudent’s t test was used to compare the variance between the groups. p < 0.05 was considered significant. Results. The system achieved stable control of muscle relaxation with a mean T1% error of −0.19 (SD 0.66) % accommodating a range in mean infusion rate (MIR) of 0.21–0.49 mg· kg⁻¹ · h⁻¹. When these results were compared with our previous ones using the same hierarchical structure applied to mivacurium, less variation in the T1% error (p < 0.05) but the same variation in infusion rate were observed. The controller activity of these two drugs showed no significant difference (p> 0.5). However, the consistent medium coefficient variance (CV) of the MIR of both rocuronium (i.e., 36.13 (SD 9.35) %) and mivacurium (i.e., 34.03 (SD 10.76)%) indicated a good controller activity. Conclusions. The results showed that a hierarchical rule-based monitoring and fuzzy logic control architecture can provide stable control of neuromuscular block despite the considerable individual variation in neuromuscular block required among patients. Also, there was less variation in T1% error compared with that of previous study on mivacurium. Meanwhile, the consistent medium CV of the MIR of both rocuronium and mivacurium indicated a good controller activity which is able to withstand noise, diathermy effect, artifacts and surgical disturbances. This revised version was published online in July 2006 with corrections to the Cover Date.     

应用模糊数学综合评价临床护理带教水平的探讨

目的：对临床护理带教水平进行评价,以期更客观地评估临床护理带教教师的带教水平,为改进临床护理带教水平测评工作提供有益参考。方法：建立临床护理带教水平评价指标体系,构建临床护理带教水平模糊综合评价模型,进行综合评价。结果：利用该模型对某位临床护理带教教师带教水平进行了评价,综合评分为83.7分,总评处于良好。结论：模糊综合评判法是一种半定性半定量分析方法,能够真实、客观地反映临床护理带教教师的带教水平,在工作中可以利用它为医院的管理决策提供服务。     

Segmentation of thalamic nuclei using a modified k-means clustering algorithm and high-resolution quantitative magnetic resonance imaging at 1.5 T

Patient outcome in minimally invasive stereotactic neurosurgical procedures depends on the ability to accurately locate the desired functional region within the deep brain while avoiding the surrounding anatomy. Due to the lack of sufficient contrast within this region in pre-operatively acquired MR images, electrophysiological exploration and histological atlases are currently required to define the surgical target within the thalamus in the treatment of many motor-control disorders. In this paper we introduce a method for segmenting the individual thalamic nuclei based on high-resolution quantitative magnetic resonance images, providing improved target visualization. The method was tested using whole-brain T1 and T2 data acquired from four healthy individuals. Accuracy of the segmentation results was assessed by comparing the center-of-mass coordinates of the segmented nuclei, with coordinates obtained from a classic histological atlas registered to these images. Strong agreement was found, with an average Euclidean distance difference of less than 4.5 mm averaged across all nuclei and all individuals. Reproducibility of the method, determined by calculating the percent similarity of segmentation results derived from data acquired from repeated scan sessions, was greater than 85%. These results illustrate the ability to accurately and reliably segment the primary nuclei of the thalamus and suggest that the method may have utility in the study of individual nuclear regions in disease state as well as for planning deep-brain surgical procedures.     

Probability Density Function Revisited: Improved Discrimination of VF Using a Cycle Length Corrected PDF

The probability density function (PDF) describes the fraction of time an electrogram signal spends at the baseline, In normal rhythm the signal is at baseline during the period between electrogram complexes, while in fibrillation the signal exhibits continuous activity and spends little time at baseline. However, time spent at the baseline is dependent on the rate of the rhythm, which limits the ability of the PDF algorithm to discriminate ventricular fibrillation from fast nonfibrillatory rhythms. A cycle length corrected version of the PDF algorithm has been formulated, which only examines the electrical activity between detected beats. The algorithm was developed utilizing a training set of 77 endocardial recordings and tested utilizing a test set of 90 endocardial and 56 epicardial recordings. Ventricular fibrillation was detected with 100% sensitivity and 98% specificity.