A new method based on Dempster–Shafer theory and fuzzy c-means for brain MRI segmentation

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

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Dempster-Shafer and Multi-Focus Image Fusion using Local Distance

10.5121/csit.2021.112206 ◽

2021 ◽

Author(s):

Ias Sri Wahyuni ◽

Rachid Sabre

Keyword(s):

Image Fusion ◽

Mass Function ◽

New Method ◽

Dempster Shafer Theory ◽

Local Distance ◽

Focus Image ◽

Fusion Images ◽

Shafer Theory ◽

Quadratic Distance ◽

Local Variability

In this article, we give a new method of multi-focus fusion images based on Dempster-Shafer theory using local variability (DST-LV). Indeed, the method takes into account the variability of observations of neighbouring pixels at the point studied. At each pixel, the method exploits the quadratic distance between the value of the pixel I (x, y) of the point studied and the value of all pixels which belong to its neighbourhood. Local variability is used to determine the mass function. In this work, two classes of Dempster-Shafer theory are considered: the fuzzy part and the focused part. We show that our method gives the significant and better result by comparing it to other methods.

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A Signature Analysis Method for IC Failure Analysis

ISTFA 1996: Conference Proceedings from the 22nd International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa1996p0189 ◽

1996 ◽

Author(s):

C.L. Henderson ◽

J.M. Soden

Keyword(s):

Failure Analysis ◽

Failure Mechanism ◽

Experiential Knowledge ◽

New Method ◽

Signature Analysis ◽

Analysis Method ◽

Dempster Shafer Theory ◽

Shafer Theory

Abstract A new method of signature analysis is presented and explained. This method of signature analysis can be based on either experiential knowledge of failure analysis, observed data, or a combination of both. The method can also be used on low numbers of failures or even single failures. It uses the Dempster-Shafer theory to calculate failure mechanism confidence. The model is developed in the paper and an example is given for its use.

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Research and Application of the Improved DST New Method Based on Fuzzy Consistent Matrix and the Weighted Average

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1030-1032.1764 ◽

2014 ◽

Vol 1030-1032 ◽

pp. 1764-1768 ◽

Cited By ~ 2

Author(s):

Wei Xiao Xu ◽

Ji Wen Tan ◽

Hong Zhan

Keyword(s):

Fault Diagnosis ◽

Evidence Synthesis ◽

Weighted Average ◽

Rolling Bearing ◽

New Method ◽

Cnc Machine ◽

Bearing Fault Diagnosis ◽

Dempster Shafer Theory ◽

High Conflict ◽

Shafer Theory

Aiming at the existing defects of evidence dempster-shafer theory (DST) in dealing with high conflict evidence, we proposed a new method to improve DST. By introducing concept of fuzzy consistent matrix, calculate the weights of factors, and put different sources of evidence into distinguish, and finally cast more than one vote to prevent the phenomenon, the average convergence of evidence. What’s more, the improved DST new method is applied to the rolling bearing fault diagnosis of CNC machine workbench .The test results show that the improved new synthetic formula increases the accuracy of fault diagnosis Ball, the conflict of evidence synthesis results better, to achieve better results.

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