An Improved Crow Search Based Intuitionistic Fuzzy Clustering Algorithm for Healthcare Applications

2019 ◽  
pp. -1--1
Author(s):  
S Parvathavarthini ◽  
N Visalakshi ◽  
S Shanthi ◽  
J Mohan
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Healthcare Applications ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Clustering Algorithm ◽  
Intuitionistic Fuzzy Clustering

scholarly journals Superpixels extraction by an Intuitionistic fuzzy clustering algorithm

2021 ◽  
Vol 19 (2) ◽  
pp. 140-152
Author(s):  
Dante Mújica Vargas
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Experimental Tests ◽  
Color Images ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Clustering Algorithm ◽  
Natural Color ◽  
Intuitionistic Fuzzy Clustering ◽  
Segmentation Task

A scheme to develop the image over-segmentation task is introduced in this paper, it considers the pixels of an image as intuitive fuzzy sets and develops an intuitionistic clustering process of them. In this regard, the main contribution is to provide a method for extracting superpixels with greater adherence to the edges of the regions. Experimental tests were developed considering biomedical grayscale and natural color images. The robustness and effectiveness of this proposal was verified by quantitative and qualitative results.

scholarly journals A Modified Intuitionistic Fuzzy Clustering Algorithm for Medical Image Segmentation

2018 ◽  
Vol 27 (4) ◽  
pp. 593-607 ◽  
Author(s):  
S.V. Aruna Kumar ◽  
B.S. Harish
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Medical Image ◽  
Clustering Algorithm ◽  
Intuitionistic Fuzzy Set ◽  
Medical Image Segmentation ◽  
Cluster Center ◽  
Intuitionistic Fuzzy ◽  
Elementary Condition ◽  
Intuitionistic Fuzzy Clustering

Abstract This paper presents a modified intuitionistic fuzzy clustering (IFCM) algorithm for medical image segmentation. IFCM is a variant of the conventional fuzzy C-means (FCM) based on intuitionistic fuzzy set (IFS) theory. Unlike FCM, IFCM considers both membership and nonmembership values. The existing IFCM method uses Sugeno’s and Yager’s IFS generators to compute nonmembership value. But for certain parameters, IFS constructed using above complement generators does not satisfy the elementary condition of intuitionism. To overcome this problem, this paper adopts a new IFS generator. Further, Hausdorff distance is used as distance metric to calculate the distance between cluster center and pixel. Extensive experimentations are carried out on standard datasets like brain, lungs, liver and breast images. This paper compares the proposed method with other IFS based methods. The proposed algorithm satisfies the elementary condition of intuitionism. Further, this algorithm outperforms other methods with the use of various cluster validity functions.

scholarly journals Interval Intuitionistic Fuzzy Clustering Algorithm Based on Symmetric Information Entropy

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 79 ◽  
Author(s):  
Jian Lin ◽  
Guanhua Duan ◽  
Zhiyong Tian
Keyword(s):  
Fuzzy Clustering ◽  
Information Entropy ◽  
Clustering Algorithm ◽  
Distance Measure ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Symmetric Information ◽  
Intuitionistic Fuzzy Clustering ◽  
Fuzzy State ◽  
Clustering Problems

Based on the continuous optimal aggregation operator, a novel distance measure is proposed to deal with interval intuitionistic fuzzy clustering problems. The optimal ordered weighted intuitionistic fuzzy quasi-averaging (OOWIFQ) operator and the continuous OOWIFQ operator are presented to aggregate all the values in an interval intuitionistic fuzzy number. Some of their desirable properties are also studied. The OOWIFQ operator can describe the fuzzy state of things more realistically and present the fuzzy properties more accurately. The opinions of experts are very important, the OOWIFQ operators take expert preferences into account to reduce systematic errors. Considering the hesitation of things and avoiding distortion of information, we put forward the distance measure for interval intuitionistic fuzzy numbers by using symmetric information entropy. Based on the continuous OOWIFQ operator and proposed distance measure, a new interval intuitionistic fuzzy clustering (IIFC) algorithm is proposed. The application in soil clustering shows the validity and practicability of the IIFC algorithm.

scholarly journals Image Segmentation Based on Intuitionistic Type-2 FCM Algorithm

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Zhongqiang Pan ◽  
Xiangjian Chen
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Experimental Results ◽  
Fcm Algorithm ◽  
Hybrid Clustering ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Clustering Algorithm ◽  
Segmentation Accuracy

Due to using the fuzzy clustering algorithm, the accuracy of image segmentation is nothigh enough. So one hybrid clustering algorithm combined with intuitionistic fuzzy factor and localspatial information is proposed. Experimental results show that the proposed algorithm is superiorto other methods in image segmentation accuracy and improves the robustness of the algorithm.

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