A rank ordered filter for medical image edge enhancement and detection using intuitionistic fuzzy set

This paper gives a novel scheme using intuitionistic fuzzy set theory to enhance the edges of medical images. Medical images contain lots of uncertainties, as they are poorly illuminated and fuzzy/vague in nature. So, direct segmentation techniques will not produce better results. There are lots of researches on edge enhancement starting from non-fuzzy to fuzzy set, but proper enhancement (highlighting important structures) is not obtained. Enhancement of edges helps in recovering the important structures that are not visible properly. Even minute pathological blood vessels/cells are not visible properly and in that case edge enhancement will enhance these blood vessels/cells. Intuitionistic fuzzy set theory is found suitable in medical image processing as it considers more (two) uncertainties as compared to fuzzy set theory. In the processing phase, image is initially converted to intuitionistic fuzzy image and intuitionistic fuzzy entropy is used to obtain the optimum value of the parameter in the membership and non-membership functions. Then it computes the total variation of the pixels with respect to the median value of the image window (rank order filtering). This enhances the borders or the edges of the image. The resulting image is then segmented (edge detected) using standard Canny's edge detector, when simply using Canny's edge detector does not give better result. From the result it is observed that on comparing with non-fuzzy and fuzzy methods, the proposed method gives better information about the images, which is helpful to the pathologists in accurate diagnosing of diseases.     

Interactive image enhancement by fuzzy relaxation

In this paper,an interactive image enhancement(IIE)technique based on fuzzy relaxation is presented,which allows the user to select different intensity levels for enhancement and intermit the enhancement process according to his/her preference in applications.First,based on an analysis of the convergence of a fuzzy relaxation algorithm for image contrast enhancement,an improved version of this algorithm,which is called FuzzIIE Method 1,is suggested by deriving a relationship between the convergence regions and the parameters in the transformations defined in the algorithm.Then a method called FuzzIIE Method 2 is introduced by using a different fuzzy relaxation function,in which there is no need to re-select the parameter values for interactive image enhancement. Experimental results are presented demonstrating the enhancement capabilities of the proposed methods under different conditions.     

一种改进的模糊软集多参数决策方法

模糊软集多参数决策方法中经常将Zadeh交与代数积使用在数据融合方法中,在一些实际应用中会产生信息缺失,导致决策者无法做出准确的选择。针对这一问题,结合Einstein运算法则提出一种新的数据融合方法,用于解决信息缺失和对象无法排序的问题。所提出的基于模糊软集的多参数决策方法是通过Einstein积运算进行多个参数集合的整合,从而得到一个合成模糊软集,再由合成模糊软集计算得到对照矩阵与得分表,最终得到对象的全排序,为决策者提供判断依据。通过实例结果,可以验证新方法在决策问题中的正确性和有效性。     

Type II fuzzy set-based data analytics to explore amino acid associations in protein sequences of Swine Influenza Virus

The veracity present in molecular data available in biological databases possesses new challenges for data analytics. The analysis of molecular data of various diseases can provide vital information for developing better understanding of the molecular mechanism of a disease. In this paper, an attempt has been made to propose a model that addresses the issue of veracity in data analytics for amino acid association patterns in protein sequences of Swine Influenza Virus. The veracity is caused by intra-sequential and inter-sequential biases present in the sequences due to varying degrees of relationships among amino acids. A complete dataset of 63,682 protein sequences is downloaded from NCBI and is refined. The refined dataset consists of 26,594 sequences which are employed in the present study. The type I fuzzy set is employed to explore amino acid association patterns in the dataset. The type I fuzzy support is refined to partially remove the inter-sequential biases causing veracity in data. The remaining inter-sequential biases present in refined fuzzy support are evaluated and eliminated using type II fuzzy set. Hence, it is concluded that a combination of type II fuzzy & refined fuzzy approach is the optimal approach for extracting a better picture of amino acid association patterns in the molecular dataset.     

Comment on: “Image thresholding using type II fuzzy sets”. Importance of this method

In this work we develop some reflections on the thresholding algorithm proposed by Tizhoosh in [16]. The purpose of these reflections is to complete the considerations published recently in [17] and [18] on said algorithm. We also prove that under certain constructions, Tizhoosh's algorithm makes it possible to obtain additional information from commonly used fuzzy algorithms.     

适用于医学图像的模糊边缘检测改进算法

边缘检测是医学图像预处理中最重要的内容之一,系统地分析了Pal.King模糊边缘检测算法的不足,提出一种适用于医学图像的新型模糊边缘检测算法。该算法使用遗传算法对传统Otsu算法进行优化来确定阈值参数,对待测图像所对应的模糊特征平面通过基于此阈值定义的隶属函数来提取,并且对图像进行分区模糊增强和平滑处理以达到更好的边缘检测效果。仿真结果表明,针对所要处理的医学图像,与几种经典的处理方法相比,该算法能够提取出更加真实和完整的边界信息。     

面向水下图像集的一致性增强评价方法

目的 在对整个水下图像集的质量进行评价时,现有方法是采用某一质量评价准则的质量分数平均值作为指标,以平均值的高低来说明质量增强算法的优劣,但是,非一致性增强的质量分数平均值会随着图像集的变化而产生较大的波动。为了解决上述问题,本文提出了一个更加具有普适性的水下图像质量评价方法：一致性增强质量评价（CEQA）方法。方法 所提方法通过对比图像增强前后的质量分数差值,来判断增强算法性能的一致性,再通过改变选定的质量分数差值所占权重比例并统一分数制,求出一致性增强的图像集的一致性增强质量评价分数。结果 虽然当图像集较小时,非一致性增强的图像质量增强算法得到的质量分数平均值最高,但当图像集扩大时,其增强后的质量分数平均值却低于原图的质量分数平均值;而在图像集扩展前后,一致性增强的图像质量增强算法能够稳定地增强图像质量,其得到的质量分数平均值一直高于原图的质量分数平均值。结论 本文通过实验证明了所提方法的可行性,扩展应用能够通过本文方法得到有效的实验数据,以对比说明各种水下图像质量增强算法的优劣;本文的方法比平均值方法更加鲁棒有效地控制了大样本偏差。因此,本文为大规模应用中如何选取水下图像集的质量增强算法,提供了一个更好的评价标准。     

改进的多层次模糊增强算法应用研究

在工业上由CCD相机拍摄的图像,因一些不利的因素,会产生斑点噪声且使待检测的目标间强度对比比较明显。对这一问题,目前常用的传统边缘检测和基于模糊理论的边缘检测方法存在着各种缺陷,由此提出了一种多层次模糊增强边缘检测算法。该算法首先使用Valley-emphasis算法来计算阈值参数,根据阈值定义的凸非线性隶属函数对待测灰度图进行模糊特征平面映射,再对模糊域进行平滑处理和模糊增强。在此基础上,提出了基于模糊熵的边缘检测方法。实验结果表明该算法有效,检测结果为工业上质量控制提供了重要依据。     

基于核模糊聚类的变分水平集医学图像分割方法

针对现有无须重新初始化的变分水平集分割模型, 存在对边缘模糊、对比度差等图像不是很敏感、分割效果不理想的问题, 提出了一种基于核模糊聚类的变分水平集医学图像分割方法。将原始图像进行核模糊C-均值聚类, 把得到的聚类结果带入初始化水平集函数得到初始轮廓, 最后利用李模型的分割方法实现最终的图像分割。实验结果表明, 该方法具有良好的分割质量, 适应性强, 同时可减少迭代次数。     

各向异性扩散的遥感图像边缘增强方法

为解决遥感图像边缘增强过程中辨识弱边缘和噪声的问题,提出一种改进的模糊各向异性边缘增强方法。根据非下采样轮廓波变换系数分布特征,获得像素几何结构信息;并基于各像素在不同子带的系数分布特征和噪声方差,分析其均值和最大值的模糊隶属度;利用模糊推理计算扩散系数,更好地控制各向异性扩散过程。实验结果显示,该方法具有更好的边缘增强和抑噪性能,能有效地辨识弱边缘和降低时间复杂度。     

模糊多尺度Retinex彩色图像增强

由于中心环绕Retinex算法中假设场景中光照是平缓变化的,所以在图像明暗对比强烈处易出现光晕现象。针对Retinex传统算法的固有缺陷,结合MSRCR算法在色彩恢复上的优势,提出了一种模糊多尺度Retinex彩色图像增强方法（FMSRCR）。FMSRCR使中心环绕空间对比运算仅在光照强度相近的区域中进行,克服了光照不均的影响。同时采用自适应高斯模,减少了卷积运算量。通过实验证明该方法是有效的。     

Image thresholding using type II fuzzy sets

Image thresholding is a necessary task in some image processing applications. However, due to disturbing factors, e.g. non-uniform illumination, or inherent image vagueness, the result of image thresholding is not always satisfactory. In recent years, various researchers have introduced new thresholding techniques based on fuzzy set theory to overcome this problem. Regarding images as fuzzy sets (or subsets), different fuzzy thresholding techniques have been developed to remove the grayness ambiguity/vagueness during the task of threshold selection. In this paper, a new thresholding technique is introduced which processes thresholds as type II fuzzy sets. A new measure of ultrafuzziness is also introduced and experimental results using laser cladding images are provided.     

Fuzzy system reliability evaluation using time-dependent intuitionistic fuzzy set

In general, fuzzy sets are used to analyse the system reliability. In this article, the concept of fuzzy set is extended by the idea of intuitionistic fuzzy set (IFS) and a new general procedure is proposed to construct the membership and non-membership functions of the fuzzy reliability using time-dependent IFS. Here, failure rate function of the system is represented by a triangular intuitionistic fuzzy number (IFN). Also, using proposed approach, membership and non-membership functions of fuzzy reliability of series and parallel systems are constructed, where the failure rate of each component is taken as a time-dependent triangular IFN. The major advantage of using IFS over fuzzy sets is that IFS separate the positive and negative evidences for membership of an element in the set. Numerical examples are given to illustrate the proposed approach.     

一种模糊核聚类的线性滤波多光谱图像增强算法

针对图像噪声过多以及模糊度过高所造成的多光谱图像视觉效果较差、图像细节难以分辨等问题,提出了一种模糊核聚类的线性滤波多光谱图像增强算法。该算法采用模糊核聚类的去噪方法,对分解图像得到的模糊系数进行了阈值处理,并引入去噪增益因子,可以有效地去除多光谱图像的噪声。在多光谱图像亮度增强上,采用了多向聚类亮度增强公式来将图像的模糊像素亮度提升至标准亮度,对图像边缘部分的亮度则采用边缘化增益方法来进行增强,最后采用线性滤波的方法来保护多光谱图像的结构张量,防止多光谱图像的结构信息发生扩散变化。实验结果表明,采用模糊核聚类的方法能够有效地去除多光谱图像噪声,在图像亮度增强上相比对比算法取得了较好的效果。     

基于中心扰动的区间值模糊集图像阈值分割算法

针对基于区间值模糊集的图像阈值分割问题,提出了一种基于中心扰动的区间值模糊集图像阈值分割算法.采用对目标及背景中心进行扰动的方式,考虑不确定、不精确信息对图像类别中心的影响,并利用限制等价函数构建图像的区间值模糊集模型;在提出一种区间值模糊集上区别度量的基础上建立目标函数来搜索最佳分割阈值.通过对三种类型的图像数据进行仿真实验,结果表明提出的方法在视觉和指标上总体得到了较好的结果,证明了该算法的有效性.     

Image contrast enhancement using fuzzy clustering with adaptive cluster parameter and sub-histogram equalization

Histogram equalization is an effective technique to boost image quality and contrast enhancement. However, in some cases the increase in image contrast by traditional histogram equalization exceeds the desired amount Which damages the image properties and wanes its natural look. Histogram division and performing a separate equalization for each sub-histogram is one of the presented solutions. The dividing method and determining the number of sub-histograms are the main problems directly affecting the output image quality. In this study, a method is introduced for automatic determination of the number of sub-histograms and density based histogram division leading to appropriate output with no need for parameter setting. Each main peak is in a separate section. Image contrast is increased with no loss of image specifications through determining the number of sub-histograms based on the number of main peaks. The introduced histogram equalization approach consists of three stages. The first stage, using histogram analysis, produces an automated estimate of number of clusters for image brightness levels. The second, clusters the image brightness levels, and using the provided transfer function, the final stage includes contrast enhancement for each individual cluster separately. The results of the proposed approach demonstrate not only clearer details along with a boost in contrast, but also noticeably more natural appearance in the images.     

最小模糊偏移度准则下的SAR图像对比度增强

针对SAR图像灰度特性,提出最小模糊偏移自动对比度增强算法(MFO)。该算法基于模糊理论,利用高斯型隶属度函数将图像灰度信息模糊化,以模糊偏移度最小准则确定模糊对比度增强操作数(INT),得到该准则下最优的S形灰度映射函数,增强SAR图像对比度。利用TEN、EME两种评估参数评价增强结果,验证了算法的有效性。     

Group decision making in medical system: An intuitionistic fuzzy soft set approach

In medical system, there may be many critical diseases, where experts do not have sufficient knowledge to handle those problems. For these cases, experts may provide their opinion only about certain aspects of the disease and remain silent for those unknown features. Feeling the need of prioritizing different experts based on their given information, this article uses a novel concept for assigning confident weights to different experts which are mainly based on their provided information. Experts provide their opinions about various symptoms using intuitionistic fuzzy soft matrix (IFSM). In this article, we propose an algorithmic approach based on intuitionistic fuzzy soft set (IFSS) which explores a particular disease reflecting the agreement of all experts. This approach is guided by the group decision making (GDM) model and uses cardinals of IFSS as novel concept. We have used choice matrix (CM) as an important parameter which is based on choice parameters of individual expert. This article has also validated the proposed approach using distance measurements and consents of the majority of experts. The effectiveness of the proposed approach is demonstrated using a suitable case study.     

Analog and digital image enhancement using fuzzy expert systems

 Image enhancement is a field that is being used in various areas and disciplines. Advances in computers, microcontrollers and DSP boards have opened new horizons to digital image processing, and have opened many avenues to the design and implementation of new innovative techniques. This paper compares image enhancement via the modification of the probability density function of the gray levels with the new techniques that involves the use of knowledge-base (fuzzy expert) systems that are capable of mimicking the behavior of a human expert. A fuzzy expert system based software for image enhancement, called SmartPhotoLab has been introduced for the above purpose. Present address: A. El-Osery Dept. of Electrical Engineering, New Mexico Tech, Workman Center Rm. 247 801 Leroy place, Socorro, NM 87801 e-mail: elosery@ee.nmt.edu. This work was supported in parts by NASA grants no. NAG2–1196 and 2-1480.     

一种改进的具有三种否定的新模糊集及其应用

对于模糊知识及其否定关系,潘指出应该明确地分为矛盾否定关系、对立否定关系和中介否定关系,并建立了一种具有矛盾否定、对立否定和中介否定的新模糊集FScom。在此基础上,对模糊知识及其各种否定的集合基础进一步研究,提出了一种改进的具有三种否定的模糊集IFScom,并讨论了它的特征、运算及相关性质。应用实例表明改进的模糊集IFScom在模糊知识的“否定”的区分、表示以及推理上是有效性的。