Interval Type-2 Relative Entropy Fuzzy C-Means clustering

Zarinbal, Marzieh; Zarandi, M. H. Fazel; Türkşen, I.B.

doi:10.1016/j.ins.2014.02.066

Cited by 34 publications

(10 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fuzzy fusion technique works on if-else rule [14]. The type-II probabilistic C-Mean (T2PCM) has been used to divide the whole MRI image into four types namely White Matter, Gray Matter, Cerebrospinal Fluid and Abnormal Region [15] [16]. The main arguments taken by T2PCM are the number of clusters in which the data is to be divided and the amount of fuzziness.…”

Section: Literature Reviewmentioning

confidence: 99%

An Evaluation of Automated Tumor Detection Techniques of Brain Magnetic Resonance Imaging (MRI)

Wahid¹,

Fayaz

Shah

2016

IJBSBT

View full text Add to dashboard Cite

Image processing is a technique developed by computer and Information technology scientist and being used in all field of research including medical sciences. The focus of this paper is the use of image processing in tumor detection from the brain Magnetic Resonance Imaging (MRI). For the brain tumor detection, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the prominent imaging techniques, but most of the experts prefer MRI over CT. The traditional method of tumor detection in MRI images is a manual inspection which provides variations in the results when analyzed by different experts, therefore, in view of the limitations of the manual analysis of MRI, there is a need for an automated system that can produce globally acceptable and accurate results. There is enough amount of published literature available to replace the manual inspection process of MRI images with the digital computer system using image processing techniques. In this paper, we have provided a review of digital image processing techniques in the context of brain MRI processing and critically analyzed them for the identification of the gaps and limitations of the techniques so that the gaps can be filled and limitations of various techniques can be improved for precise and better results.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

An Evaluation of Automated Tumor Detection Techniques of Brain Magnetic Resonance Imaging (MRI)

Wahid¹,

Fayaz

Shah

2016

IJBSBT

View full text Add to dashboard Cite

show abstract

“…Zarandi (2014a, 2014b) proposed an algorithm of general type-2 fuzzy clustering for analyzing gene expression data with newly developed general type-2 cluster validity index. Zarinbal et al (2014) proposed Interval Type-2 Relative Entropy FCM in which the uncertainty associated with membership functions is the main concern and an application to MR image segmentation was discussed.…”

Section: Literature Reviewmentioning

confidence: 99%

Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection

Ngo

Mai

Pedrycz

2015

Computers & Geosciences

View full text Add to dashboard Cite

“…At present, the theoretical study of interval type-2 fuzzy sets (IT2FS) mainly focuses on pure mathematics. For example, Chen [9] studied the nature and operation of IT2FS, Zheng et al [10] analyzed the similarity and acceptance of IT2FS, Zarinbal et al [11] proposed a clustering analysis model of IT2FS with the method of relative entropy, Hwang et al [12] proposed a similarity measurement method of interval type-2 fuzzy entropy, and Li et al [13] proposed the uncertainty measurement method of IT2FS. These theoretical studies serve as a good theoretical foundation for the research on IT2FS in the field of multiattribute decision making (MADM).…”

Section: Literature Reviewmentioning

confidence: 99%

The Likelihood Ranking Methods for Interval Type-2 Fuzzy Sets Considering Risk Preferences

Zhao

Qin

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper proposes a ranking method that considers the risk preferences of decision makers for multiple-attribute decision-making problems in a multiple-interval type-2 trapezoidal fuzzy set environment. First, decision makers are classified according to the risk preferences and a measurement method of risk preferences is proposed. Second, a risk preference decision matrix is obtained and a new calculation formula of likelihood is defined. Finally, we obtain the ranking results of alternatives by calculating the signed distance. Our example analysis shows that the proposed method is scientific and reasonable, and different risk preferences influence the results of decision making. Comparison with previous methods shows that the proposed algorithm is more feasible; it is applicable for decision making on both risk preferences and risk conservation.

show abstract

Interval Type-2 Relative Entropy Fuzzy C-Means clustering

Cited by 34 publications

References 29 publications

An Evaluation of Automated Tumor Detection Techniques of Brain Magnetic Resonance Imaging (MRI)

An Evaluation of Automated Tumor Detection Techniques of Brain Magnetic Resonance Imaging (MRI)

Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection

The Likelihood Ranking Methods for Interval Type-2 Fuzzy Sets Considering Risk Preferences

Contact Info

Product

Resources

About