An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation

Verma, Hanuman; Agrawal, R. K.; Sharan, Aditi

doi:10.1016/j.asoc.2015.12.022

Cited by 159 publications

(69 citation statements)

References 30 publications

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“…The drawback of standard fuzzy clustering is that it does not include any spatial information for segmentation. In Verma et al (), an improved Intuitionistic FCM (IFCM) clustering algorithm, that incorporates the local spatial information and local gray level information in IFCM. The splitting techniques of Discrete Curve Evolution (DCE) techniques are used to find cluster for T1, T2 and PD MR Brain image segmentation.…”

Section: Brain Tumour Segmentation Techniques Of Mrimentioning

confidence: 99%

Automated brain tumour segmentation techniques— A review

Angulakshmi

Priya

2017

Int J Imaging Syst Tech

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Automatic segmentation of brain tumour is the process of separating abnormal tissues from normal tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The process of segmentation is still challenging due to the diversity of shape, location, and size of the tumour segmentation. The metabolic process, psychological process, and detailed information of the images, are obtained using positron emission tomography (PET) image, Computer Tomography (CT) image and Magnetic Resonance Image (MRI). Multimodal imaging techniques (such as PET/CT and PET/MRI) that combine the information from many imaging techniques contribute more for accurate brain tumour segmentation. In this article, a comprehensive overview of recent automatic brain tumour segmentation techniques of MRI, PET, CT, and multimodal imaging techniques has been provided. The methods, techniques, their working principle, advantages, their limitations, and their future challenges are discussed in this article.

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Section: Brain Tumour Segmentation Techniques Of Mrimentioning

confidence: 99%

Automated brain tumour segmentation techniques— A review

Angulakshmi

Priya

2017

Int J Imaging Syst Tech

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“…Chuang et al [20] proposed fuzzy c-implies grouping with spatial data to manage power non-consistency and to expel uproarious spots amid picture division. Yang et al [21], LiMaand Staunton [22], Jiayin Kang et al [23], and Zhou Xiancheng et al [24] have proposed novel altered fuzzy c-implies calculation by joining the spatial neighborhood data into the standard FCM calculation to evacuate force inhomogeneities in therapeutic pictures. Anupama Namburu et.al defines basic implementation of MR brain image segmentation based on fuzzy rough and Rough sets.…”

Section: Review Of Related Workmentioning

confidence: 99%

Enhanced and Explored Intuitionistic Rough Based Fuzzy C-means Approach for MR Brain Image Segmentation

Prasanthi¹,

Nagamalleswararao²

2018

IJET

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Segmentation of magnetic resonance images is medically complex and important for study and diagnosis of medical brain images, because of its sensitivity in terms of noise for brain medical images. These are the main issues in classification of brain images. Because of uncertainty & vagueness of brain medical images, so that rough sets, fuzzy sets and Rough sets are mathematical tools evaluate and handle uncertainty and vagueness in medical brain images. Traditionally, different type of fuzzy sets, Rough sets and rough sets based approaches were introduced, they have different several drawbacks with respect to different parameters. So this paper introduces a novel image segmentation calculation method i.e. Enhanced and Explored Intuitionistic Rough based Fuzzy C-means Approach (EEISFCMA) with estimation of weight bias parameter for brain image segmentation. Intuitionistic Rough based fuzzy sets are generalized form of fuzzy, Rough sets and their representative elements are evaluated with non-membership and membership value. Proposed algorithm of this paper consists standard features of existing clustering without spatial weight context data, it defines sensitive of noise in brain images, so that our proposed algorithm deals with intensity and noise reduction of brain image effectively. Furthermore, to reduce iterations in clustering, proposed algorithm initializes cluster centroid based on weight measure using max-dist evaluation method before execution of proposed algorithm. Experimental results of proposed approach carried out efficient image segmentation results compared to existing segmented approaches developed in brain image and other related images. Mainly proposed approach have consists better experimental evaluation based on results.

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“…The Fuzzy C-Means (FCM) algorithm is another automatic clustering technique which has been widely accepted to segment brain tissue into white matter, grey matter and cerebrospinal fluid with high degree of efficiency, this is principally because of FCM's ability to classify pixels into varying cluster regions based on degree of membership [17]. Despite, FCM suffers from high sensitivity to noise and cluster centroid determination.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Gaussian Mixture Model and Spatial Fuzzy C-Means for Brain MR Image Segmentation

Oluwasanmi¹,

Qin²,

Lan³

2018

dtcse

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Brain image segmentation into white matter, grey matter and cerebrospinal fluid is a very popular yet challenging area in medical image processing. The Fuzzy C-Means clustering algorithm is quite used because of its ability to ensure multiple member of pixels in several clusters. This is further appreciated when the spatial information of the data is considered, as such, the algorithm becomes much more robust to noise. However, pixels which form part of the non-overlapped tissues often come out inaccurate. To solve this problem, a spatial fuzzy algorithm fused with the Gaussian mixture model using the expectation maximization algorithm is presented in this paper. The proposed algorithm is a fusion of the Fuzzy C-Means and gaussian Mixture Model algorithms for segmenting tissues having multiple cluster memembership as well as single clustering. The results of the proposed algorithm are compared with the other classical algorithms against the manually segmented results of the input image. The estimated results of the proposed algorithm using the Dice and Jaccard similarity index indicates improved accuracy to the other algorithms.

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An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation

Cited by 159 publications

References 30 publications

Automated brain tumour segmentation techniques— A review

Automated brain tumour segmentation techniques— A review

Enhanced and Explored Intuitionistic Rough Based Fuzzy C-means Approach for MR Brain Image Segmentation

Fusion of Gaussian Mixture Model and Spatial Fuzzy C-Means for Brain MR Image Segmentation

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