Intuitionistic fuzzy local information C-means algorithm for image segmentation

Image segmentation allows us to separate an image into distinct, non-overlapping parts by utilizing specific features such as hue, texture, and shape. The technique is prevalent in different domains, including target detection, medical imaging, and pattern recognition owing to its importance in anal...

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Vydáno v:Information sciences Ročník 681; s. 121205
Hlavní autoři: Cui, Hanshuai, Xie, Zheng, Zeng, Wenyi, Ma, Rong, Zhang, Yinghui, Yin, Qian, Xu, Zeshui
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.10.2024
Témata:
Clustering
Distance measure
Fuzzy C-means algorithm
Image segmentation
Intuitionistic fuzzy set
GKWFLICM
nIFCM
MRI
IFLICM
Fuzzy C-means algorithm
RLFCM
FLICM
DSFCM_N
mIFCM
IFS
Double DIP
Distance measure
IT2FCM
IT2PFCM
IFCM
KWFLICM
FCM_S2
FCM_S1
Clustering
FCM
Image segmentation
Intuitionistic fuzzy set
FGFCM
IIFCM
EnFCM
FCM_S
ISSN:0020-0255, 1872-6291
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Popis
Shrnutí:Image segmentation allows us to separate an image into distinct, non-overlapping parts by utilizing specific features such as hue, texture, and shape. The technique is prevalent in different domains, including target detection, medical imaging, and pattern recognition owing to its importance in analyzing the image. The fuzzy C-means (FCM) algorithm is a popular method for image segmentation and pattern recognition. However, uncertainty and unknown noise in the data impair the effectiveness of the algorithm. Alternatively, uncertainty in real world can be addressed by the intuitionistic fuzzy set (IFS). This article presents a new approach to image representation using IFS and local information about the image. We introduce the concept of filtering into the intuitionistic fuzzy set and utilize a specially designed exponential distance for IFS. We propose the intuitionistic fuzzy local information C-means (IFLICM) algorithm. The goal of IFLICM is to increase the tolerance to noise and the maintain the details in image better than existing FCM variants. We test the performance of our algorithm on a public dataset and compare it with existing FCM methods and Double Deep-Image-Prior (Double-DIP). The experimental results demonstrate that IFLICM is highly effective in image segmentation and outperforms existing methods. •We use an IFS to represent uncertainty, combine local information propose a novel image representation method.•We propose an exponential distance measure for IFSs, and show its superiority by illustrating examples.•We develop a novel image segmentation algorithm, intuitionistic fuzzy local information C-means (IFLICM).•We complete performance evaluation, the results show great improvement and robustness of proposed method.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2024.121205