Chest X-ray enhancement to interpret pneumonia malformation based on fuzzy soft set and Dempster–Shafer theory of evidence

Image enhancement algorithms are commonly used to increase the contrast and visual quality of low-dose x-ray images. This paper proposes an automated enhancement method using soft fuzzy sets with a new decision-making scheme based on Dempster-Shafer theory of evidence for the visual interpretation of pneumonia malformation in low-dose x-ray images, called as XEFSDS. The XEFSDS model first generates an original source x-ray image into a complementary image, then each original and complement image is applied to the characterized image object and background areas of fuzzy space. The S-function is utilized to define fuzzy soft sets for the classification of gray level ambiguity in both images, and hence a decision criterion via Dempster-Shafer approach and fuzzy interval has been adapted to discriminate uncertainties on the pixel intensity and the spatial information. Modified membership grade operations have been performed on each object/background area, and Werner’s AND/OR operator (an aggregation operator) has been utilized to build a new membership function from two modified membership functions. Finally, an enhanced image is obtained from the new membership function via defuzzification. Experiments on different pneumonia X-ray images demonstrate that the XEFSDS scheme produces better results than the existing methods. To show the advantages of the XEFSDS scheme, we have executed a segmentation based examination on enhanced image for the detection of pneumonia malformation as well as abnormal lobe (lobar pneumonia) or bronchopneumonia.     

A new probability transformation method based on a correlation coefficient of belief functions

The Dempster-Shafer evidence theory is widely used in many fields of information fusion because of its advantage in handling uncertain information. One of the key issues in this theory is how to make decision based on a basic probability assignment (BPA). Currently, a feasible scheme is transforming a BPA to a distribution of probabilities. However, little attention was paid to the correlation between BPA and probability distribution. In this paper, a novel method about the probability transformation based on a correlation coefficient of belief functions is proposed. The correlation coefficient is a new measurement, which can effectively measure the correlation between BPAs. The proposed method aims at maximizing the correlation coefficient between the given BPA and the transformed probability distribution. On the basis of this idea, the corresponding probability distribution can be obtained and could reflect the original information of the given BPA to the maximum extent. It is valid to consider that the proposed probability transformation method is reasonable and effective. Numerical examples are given to show the effectiveness of the proposed method.     

UNCERTAINTY IN THE DEMPSTER-SHAFER THEORY: A CRITICAL RE-EXAMINATION

Measures of two types of uncertainty that coexist in the Dempster-Shafer theory are overviewed. A measure of one type of uncertainty, which expresses nonspecificity of evidential claims, is well justified on both intuitive and mathematical grounds. Proposed measures of the other types of uncertainty, which atlempt to capture conflicts among evidential claims, are shown to have some deficiencies. To alleviate these deficiencies, a new measure is proposed. This measure, which is called a measure of discord, is not only satisfactory on intuitive grounds, but has also desirable mathematical properties. A measure of total uncertainty, which is defined as the sum of nonspecificity and discord, is also discussed. The paper focuses on conceptual issues. Mathematical properties of the measure of discord are only slated; their proofs are given in a companion paper.¹⁵     

A NOTE ON UNCERTAINTY,DEMPSTER RULE OF COMBINATION,AND CONFLICT

Abstract

The relationship between uncertainty and the Dempster rule of combination in Dempster-Shafer theory is discussed. In particular, a condition is presented that is sufficient to guarantee that uncertainty does not increase by using the rule. A new definition of conflict of pieces of evidence is proposed and discussed.     

Support for argument structures review and assessment

Argument structures are commonly used to develop and present cases for safety, security and for other properties of systems. Such structures tend to grow excessively, which causes problems with their review and assessment. Two issues are of particular interest: (1) systematic and explicit assessment of the compelling power of an argument, and (2) communication of the result of such an assessment to relevant recipients. The paper presents a solution to these problems. The method of Visual Assessment of Arguments (VAA), being this solution, is based on the Dempster-Shafer theory of evidence applied to the assessment of the strength of arguments, and a visual mechanism of issuing and presenting assessments, supported by the so-called opinion triangle. In the paper we explain theoretical grounding for the method and provide guidance on its application. The results of some validation experiments are also presented.     

Effective Context Inconsistency Elimination Algorithm Based on Feedback and Reliability Distribution for loV

In recent years, context aware technology has been widely used in many fields, such as internet of vehicles （IoV）. Consistent context information plays a vital role in adapting a system to rapidly changing situations. However, sensor＇s precision variance, equipment heterogeneity, network delay and the difference of statistical algorithms can lead to inconsistency context and inappropriate services. In this paper, we present an effective algorithm of context inconsistent elimination which is based on feedback and adjusted basic reliability distribution. Through feedback, each sensor＇s perception precision can be obtained, and with the adjusted basic reliability distribution scheme, we can make full use of all context information by adjusting the influence of every context on whole judgment based on sensor＇s perception precision and threshold of sensor＇s perception precision, and then eliminate context inconsistency. In order to evaluate the performance of the proposed context inconsistency elimination algorithm, context aware rate is defined. The simulation results show that the proposed context inconsistency elimination algorithm can obtain the best context aware rate in most cases for the varied error rates of sensors.     

Fault Diagnosis Based on the Updating Strategy of Interval-Valued Belief Structures

This paper presents the dynamic method for fault diagnosis based on the updating of Interval-valued belief structures （IBSs）. The classical Jeffrey＇s updating rule and the linear updating rule are extended to the framework of IBSs. Both of them are recursively used to generate global diagnosis evidence with the form of Interval basic belief assignment （IBBA） by updating the previous evidence with the incoming evidence. The diagnosis decision can be made by global diagnosis evidence. In the process of evidence updating, the similarity factors of evidence are used to determine switching between the extended Jef frey＇s and linear updating rules, and to calculate the linear combination weights. The diagnosis examples of machine rotor show that the proposed method can provide more reliable and accurate results than the diagnosis methods based on Dempster-Shafer evidence theory.     

基于相关系数的证据冲突度量方法

当证据高度冲突时,使用D-S组合规则将会得到不合常理的结果,因此,针对证据组合规则的改进成为一个重要的研究课题,在选择合适的证据组合方法之前,确定证据之间是否冲突、冲突程度如何,是个至关重要的问题,为了更加准确地描述证据间的冲突,在分析证据相似度与证据冲突关系的基础上,基于证据相关系数提出了一种新的证据冲突度量方法,理论分析和数值计算表明该方法可有效表示证据之间的冲突程度,相关系数越大,证据间冲突就越小,与其他方法相比,该方法更具全面性和合理性。     

一种基于D-S 和ARIMA 的多模型软测量方法

针对传统软测量方法存在的预测性能差、融合能力低等缺点, 提出一种基于证据理论(D-S) 合成规则和差分自回归滑动平均(ARIMA) 模型的多模型软测量方法. 首先利用自适应模糊核聚类方法和最小二乘支持向量机建立多个子模型; 然后利用D-S 合成规则构造的概率分配函数作为权值因子, 对子模型输出进行融合以得到多模型的输出; 最后结合ARIMA 模型对静态多模型输出进行动态校正. 仿真研究与工业应用的结果表明, 所提出的方法具有良好的预测性能和融合能力.

     

一种车载自组织网络路况信息的数据信任模型

动态寻路是解决城市交通拥堵的重要手段。在动态寻路中,一些车辆产生和转发路况信息,使其他车辆能够避开拥堵路段。但一般车载自组织网络寻路算法缺乏验证路况信息的真实性的措施,导致恶意车辆能轻易篡改路况信息,误导其他车辆选择错误路线。提出了一种路况信息鉴别模型,该模型将基于数据的信任模型应用到路况信息真伪的鉴别中,并在一般投票算法的基础上利用D-S理论增加不确定情况下的鲁棒性。仿真实验表明,该算法在不增加额外信息交互的前提下,有效规避了恶意伪造信息,改进了车辆的行程时间。