检索结果-内蒙古大学图书馆

Generalized rough fuzzy c-means algorithm for brain MR image segmentation

cOMPUTER METHODS AND PROGRAMS IN BIOMEDIcINE 2012年第2期108卷 644-655页

作者： Ji, Zexuan Sun, Quansen Xia, Yong chen, Qiang Xia, Deshen Feng, Dagan Nanjing Univ Sci & Technol Sch Comp Sci & Technol Nanjing 210094 Jiangsu Peoples R China Univ Sydney Biomed & Multimedia Informat Technol BMIT Res Grp Sch Informat Technol Sydney NSW 2006 Australia Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Hong Kong Polytech Univ CMSP Dept Elect & Informat Engn Hong Kong Hong Kong Peoples R China Shanghai Jiao Tong Univ Med X Res Inst Shanghai 200025 Peoples R China

fuzzy sets and rough sets have been widely used in many clustering algorithms for medical image segmentation, and have recently been combined together to better deal with the uncertainty implied in observed image data. Despite of their wide spread applications, traditional hybrid approaches are sensitive to the empirical weighting parameters and random initialization, and hence may produce less accurate results. In this paper, a novel hybrid clustering approach, namely the generalized rough fuzzy c-means (GRFcM) algorithm is proposed for brain MR image segmentation. In this algorithm, each cluster is characterized by three automatically determined rough-fuzzy regions, and accordingly the membership of each pixel is estimated with respect to the region it locates. The importance of each region is balanced by a weighting parameter, and the bias field in MR images is modeled by a linear combination of orthogonal polynomials. The weighting parameter estimation and bias field correction have been incorporated into the iterative clustering process. Our algorithm has been compared to the existing rough c-means and hybrid clustering algorithms in both synthetic and clinical brain MR images. Experimental results demonstrate that the proposed algorithm is more robust to the initialization, noise, and bias field, and can produce more accurate and reliable segmentations. (c) 2011 Elsevier Ireland Ltd. All rights reserved.

关键词： Brain magnetic resonance image Image segmentation Intensity inhomogeneity fuzzy c-means algorithm Rough set Rough-fuzzy c-means algorithm

来源：评论

学校读者我要写书评

暂无评论

A new segmentation method of cerebral MRI images based on the fuzzy c-means algorithm

引用

TURKISH JOURNAL OF ELEcTRIcAL ENGINEERING AND cOMPUTER ScIENcES 2017年第4期25卷 3215-3226页

作者： Abderrezak, Mohamed Zaki chibane, Mouatez Billah Mansour, Karim Freres Mentouri Univ Dept Elect Engn Lab Study Elect Mat Med Applicat Constantine Algeria

The aim of this work is to present a new method for cerebral MRI image segmentation based on modification of the fuzzy c-means (FcM) algorithm. We used local and nonlocal information distance in the initial function of the robust FcM model. The obtained results of the classification of MRI images showed the effectiveness of the suggested model. calculation of the similarity index confirms that our method is well adapted to MRI images even in the presence of noise.

关键词： Segmentation magnetic resonance imaging fuzzy c-means algorithm

来源：评论

学校读者我要写书评

暂无评论

Remote sensing image classification based on semi-supervised adaptive interval type-2 fuzzy c-means algorithm

引用

cOMPUTERS & GEOScIENcES 2019年 131卷 132-143页

作者： Xu, Jindong Feng, Guozheng Zhao, Tianyu Sun, Xiao Zhu, Meng YanTai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China

Because of the uncertainty in remote sensing images and the ill-posedness of the problem, it is difficult for traditional unsupervised classification algorithms to create an accurate classification model. In contrast, pattern recognition methods based on fuzzy set theory, such as fuzzy c-means clustering, can manage the fuzziness of data effectively. Of these methods, the type-2 fuzzy c-means algorithm is better able to control uncertainty. Furthermore, semi-supervised training can use prior knowledge to deal with ill-posedness, and hence is more suitable. Therefore, we propose a novel classification method based the semi-supervised adaptive interval type-2 fuzzy c-means algorithm (SS-AIT2FcM). First, by integrating the semi-supervised approach, an evolutional fuzzy weight index m is proposed that improves the robustness and well-posedness of the model used in the clustering algorithm. This makes the algorithm suitable for remote sensing images with severe spectral aliasing, large coverage areas, and abundant features. In addition, soft constraint supervision is performed using a small number of labeled samples, which optimizes the iterative process of the algorithm and determines the optimal set of features for the data. This further reduces the ill-posedness of the model itself. The experimental data consist of three study areas: SPOTS imagery from Big Hengqin Island, Guangdong, china, and the Summer Palace, Beijing, china, as well as TM imagery from Hengqin Island. compared with several state-of-the-art fuzzy classification algorithms, our algorithm improves classification accuracy by more than 5% overall and obtains clearer boundaries in remote sensing images with serious mixed pixels. Moreover, it is able to suppress the phenomenon of isomorphic spectra.

关键词： fuzzy c-means algorithm Remote sensing image classification Semi-supervised Type-2 fuzzy set

来源：评论

学校读者我要写书评

暂无评论

Hesitant fuzzy c-means algorithm and its application in image segmentation

引用

JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2020年第3期39卷 3681-3695页

作者： Zeng, Wenyi Ma, Rong Yin, Qian Zheng, Xin Xu, Zeshui Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Sichuan Univ Chengdu Business Sch Chengdu Peoples R China

Image segmentation plays an important role in many fields such as computer vision, pattern recognition, machine learning and so on. In recent years, many variants of standard fuzzy c-means (FcM) algorithm have been proposed to explore how to remove noise and reduce uncertainty. In fact, there are uncertainty on the boundary between different patches in images. considering that hesitant fuzzy set is a useful tool to deal with uncertainty, in this paper, we merge hesitant fuzzy set with fuzzy c-means algorithm, introduce a new kind of method of fuzzification and defuzzification of image and the distance measure between hesitant fuzzy elements of pixels, present a method to establish hesitant membership degree of hesitant fuzzy element, and propose hesitant fuzzy c-means (HFcM) algorithm. Finally, we compare our proposed HFcM algorithm with some existing fuzzy c-means (FcM) algorithms, and apply HFcM algorithm in natural image, BSDS dataset image, different size images and multi-attribute decision making. These numerical examples illustrate the validity and applicability of our proposed algorithm including its comprehensive performance, reducing running time and almost without loss of accuracy.

关键词： Hesitant fuzzy set fuzzy c-means algorithm hesitant fuzzy c-means algorithm image segmentation information fusion

来源：评论

学校读者我要写书评

暂无评论

The range of the value for the fuzzifier of the fuzzy c-means algorithm

引用

PATTERN REcOGNITION LETTERS 2012年第16期33卷 2280-2284页

作者： Huang, Ming Xia, Zhixun Wang, Hongbo Zeng, Qinghua Wang, Qian Natl Univ Def Technol Sci & Technol Scramjet Lab Changsha 410073 Hunan Peoples R China Xian Satellite Control Ctr Xian 710043 Peoples R China NW Inst Nucl Technol Xian 710024 Peoples R China

The fuzzy c-means algorithm (FcM) is a widely used clustering algorithm. It is well known that the fuzzifier, m, which is also called fuzzy weighting exponent, has a significant impact on the performance of the FcM. Most of the researches have shown that there exists an effective range of the value for m. However, since the method adopted by researchers is mainly experimental or empirical, it is still an open problem how to select an appropriate fuzzifier m in theory when implementing the FcM. In this paper, we propose a theoretical approach to determine the range of the value of m. This approach utilizes the behavior of membership function on two data points, based on which we reveal the partial relationship between the fuzzifier m and the dataset structure. (c) 2012 Elsevier B.V. All rights reserved.

关键词： fuzzy c-means algorithm Fuzzifier The range of the value The behavior of membership function

来源：评论

学校读者我要写书评

暂无评论

classification of Defects in a Thermal Barrier coating Layer using the fuzzy c-means algorithm

引用

INTERNATIONAL JOURNAL OF PREcISION ENGINEERING AND MANUFAcTURING 2015年第1期16卷 53-57页

作者： Kim, Pyeong-Ho Kim, Jeong-Suk Park, Jin-Hyo Lee, Ku-Hyeun Song, Yo-Seung Lee, Deuk-Yong Pusan Natl Univ Sch Mech Engn ERC NSDM Pusan 609735 South Korea Korea Inst Machinery & Mat Surface Technol Res Ctr Chang Won 642831 Gyeongnam South Korea Korea Aerosp Univ Dept Mat Engn Goyang Si 412791 Gyeonggi Do South Korea Daelim Univ Dept Mat Engn Anyang Si 431715 Gyeonggi Do South Korea

A thermally grown oxide (TGO) layer of Al2O3 is formed between a coNicrAlY bond coating and a zirconia top coating of a thermal barrier coating (TBc) system on an Inconel 738 substrate during exposure at 1050 degrees c. Thick TGO is vulnerable to damage in terms of cracking and spallation. In order to estimate the TBc failure, fundamental damage data of TGO that is induced by compressed air are monitored to determine the failure mode and the state of damage by using a nondestructive acoustic emission (AE) system. The defects of the TGO are detected and evaluated by means of AE signal analysis with the root mean squared value in the frequency range of 100 kHz to 400 kHz. The thickness of the TGO increased with the oxidation time. The RMS (Root Mean Square) value decreased almost linearly as the TGO thickness increased up to the failure of the TBc. The amplitude of the AE signal decreased dramatically when the TGO was delaminated. The AE signals for pattern classification were evaluated in accordance with the TBc layer. It is conceivable that the center value represents the damage state of the TBc coating.

关键词： Thermally grown oxide Thermal barrier coating Inconel 738 Acoustic emission fuzzy c-means algorithm

来源：评论

学校读者我要写书评

暂无评论

An new initialization method for fuzzy c-means algorithm

引用

fuzzy OPTIMIZATION AND DEcISION MAKING 2008年第4期7卷 409-416页

作者： Zou, Kaiqi Wang, Zhiping Hu, Ming Dalian Univ Technol Coll Informat Engn Univ Key Lab Informat Sci & Ingn Dalian 116622 Peoples R China

In this paper an initialization method for fuzzy c-means (FcM) algorithm is proposed in order to solve the two problems of clustering performance affected by initial cluster centers and lower computation speed for FcM. Grid and density are needed to extract approximate clustering center from sample space. Then, an initialization method for fuzzy c-means algorithm is proposed by using amount of approximate clustering centers to initialize classification number, and using approximate clustering centers to initialize initial clustering centers. Experiment shows that this method can improve clustering result and shorten clustering time validly.

关键词： fuzzy c-means algorithm Grid Density

来源：评论

学校读者我要写书评

暂无评论

A Generalized Approach to the Suppressed fuzzy c-means algorithm

A Generalized Approach to the Suppressed Fuzzy <i>c</i>-Mean...

引用

7th International conference on Modeling Decisions for Artificial Intelligence (MDAI)

作者： Szilagyi, Laszlo Szilagyi, Sandor M. Kiss, csilla Sapientia Hungarian Sci Univ Transylvania Fac Tech & Human Sci Targu Mures Romania

ISBN: (纸本)9783642162916

Suppressed fuzzy c-means (s-FcM) clustering was introduced with the intention of combining the higher convergence speed of hard c-means (HcM) clustering with the finer partition quality of fuzzy c-means (FcM) algorithm. Suppression modifies the FcM iteration by creating a competition among clusters: lower degrees of memberships are reduced via multiplication with a previously set constant suppression rate, while the largest fuzzy membership grows by swallowing all the suppressed parts of the small ones. Suppressing the FcM algorithm was found successful in terms of accuracy and working time. In this paper we introduce some generalized formulations of the suppression rule, leading to an infinite number of new clustering algorithms. Based on a large amount of numerical tests performed in multidimensional environment, some generalized forms of suppression proved to give more accurate partitions than FcM and s-FcM.

关键词： fuzzy c-means algorithm suppressed fuzzy c-means algorithm competitive clustering context sensitive suppression rules

来源：评论

学校读者我要写书评

暂无评论

Performance research of Gaussian function weighted fuzzy c-means algorithm

Performance research of Gaussian function weighted fuzzy C-m...

引用

conference on Pattern Recognition and computer Vision

作者： Liu, Xiaofang Li, Xiaowen Yang, chun He, Binbin Zhang, Ying Univ Elect Sci & Technol China Inst Geospatial Informat Sci & Technol Chengdu 610054 Peoples R China Sichuan Univ Dept Comp Sci & Technol Zigong 643000 Peoples R China Sichuan Univ Dept Engn Management Zigong 643000 Peoples R China

ISBN: (纸本)9780819469526

fuzzy c-means (FcM) algorithm is a fuzzy pattern recognition method. clustering precision of the algorithm is affected by its equal partition trend for data set of large discrepancy of each class samples number, and the optimal clustering result of the algorithm mightn't be a right partition in this case. In order to overcome this disadvantage, a Gaussian function Weighted fuzzy c-means (WFcM) algorithm is proposed, which the weighted function is produced by a Gaussian function calculating dot density of each sample. To certain extent, the WFcM algorithm has not only overcome the limitation of equal partition trend in fuzzy cmeans algorithm, but also been favorable convergence and stability. The calculation of the weighted function and the choice of sample dot density range restriction value for the algorithm are both objective. When partially supervised information obtained from a few labeled samples is introduced to the WFcM algorithm, the classification performance of the WFcM algorithm is further enhanced and the convergent speed of objective function is further accelerated.

关键词： fuzzy c-means algorithm weighted fuzzy c-means algorithm partially supervised and weighted fuzzy c-means algorithm Gaussian function pattern recognition fuzzy clustering

来源：评论

学校读者我要写书评

暂无评论

Analytical and numerical evaluation of the suppressed fuzzy c-means algorithm: a study on the competition in c-means clustering models

Analytical and numerical evaluation of the suppressed fuzzy ...

引用

5th International conference on Modeling Decisions for Artificial Intelligence

作者： Szilagyi, Laszlo Szilagyi, Sandor M. Benyo, Zoltan Sapientia Univ Transylvania Fac Tech & Human Sci Corunca 547367 Romania Budapest Univ Technol & Econ Dept Control Engn & Informat Technol H-1117 Budapest Hungary

ISBN: (纸本)3540882685

Suppressed fuzzy c-means (s-FcM) clustering was introduced in Fan et al. (Pattern Recogn Lett 24: 1607-1612, 2003) with the intention of combining the higher speed of hard c-means (HcM) clustering with the better classification properties of fuzzy c-means (FcM) algorithm. The authors modified the FcM iteration to create a competition among clusters: lower degrees of memberships were diminished according to a previously set suppression rate, while the largest fuzzy membership grew by swallowing all the suppressed parts of the small ones. Suppressing the FcM algorithm was found successful in the terms of accuracy and working time, but the authors failed to answer a series of important questions. In this paper, we clarify the view upon the optimality and the competitive behavior of s-FcM via analytical computations and numerical analysis. A quasi competitive learning rate (QLR) is introduced first, in order to quantify the effect of suppression. As the investigation of s-FcM's optimality did not provide a precise result, an alternative, optimally suppressed FcM (Os-FcM) algorithm is proposed as a hybridization of FcM and HcM. Both the suppressed and optimally suppressed FcM algorithms underwent the same analytical and numerical evaluations, their properties were analyzed using the QLR. We found the newly introduced Os-FcM algorithm quicker than s-FcM at any nontrivial suppression level. Os-FcM should also be favored because of its guaranteed optimality.

关键词： fuzzy c-means algorithm Suppressed fuzzy c-means algorithm competitive clustering Alternating optimization Learning rate

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：