Robust FCM clustering algorithm with combined spatial constraint and membership matrix local information for brain MRI segmentation

FCM Clustering Segmentation Algorithms Based on Spatial Constraint

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.411.497 ◽

2011 ◽

Vol 411 ◽

pp. 497-500

Author(s):

Jun Wei Tian ◽

Ya Lin Yu ◽

Tao Shen

Keyword(s):

Segmentation Algorithm ◽

Membership Degree ◽

Spatial Constraint ◽

Segmentation Algorithms ◽

Fcm Clustering ◽

Membership Matrix

In order to improve the performance of FCM clustering segmentation algorithm, a new spatial constrained FCM (SCFCM) algorithm is proposed. The new algorithm computes the membership degree of each pixel according to membership degrees of its neighbor pixels. For the purpose of enhance compute efficiency, two kinds of initial membership matrix creation algorithms are proposed to reduce iteration times. Experiments for a series of images are performed, according to the results, SCFCM clustering segmentation algorithm can restrain the noise effectively.

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Brain MRI segmentation for tumor detection using Grammatical Swarm based clustering algorithm

2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014] ◽

10.1109/iccpct.2014.7055051 ◽

2014 ◽

Cited By ~ 1

Author(s):

Tapas Si ◽

Arunava De ◽

Anup Kumar Bhattacharjee

Keyword(s):

Clustering Algorithm ◽

Brain Mri ◽

Tumor Detection ◽

Mri Segmentation

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A Local Information-Based Fuzzy C-Means for Brain MRI Segmentation

Advances in Intelligent Systems and Computing - Computational Intelligence: Theories, Applications and Future Directions - Volume II ◽

10.1007/978-981-13-1135-2_46 ◽

2018 ◽

pp. 607-619

Author(s):

Anu Mangla ◽

Chandan Singh

Keyword(s):

Brain Mri ◽

Local Information ◽

Mri Segmentation ◽

Fuzzy C Means

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A new validity function of FCM clustering algorithm based on intra-class compactness and inter-class separation

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210555 ◽

2021 ◽

pp. 1-22

Author(s):

H.Y. Wang ◽

J.S. Wang ◽

L.F. Zhu

Keyword(s):

Clustering Algorithm ◽

Optimal Number ◽

Data Sets ◽

Data Set ◽

Class Separation ◽

Data Similarity ◽

Fcm Clustering ◽

Membership Matrix ◽

Clustering Validity ◽

Optimal Number Of Clusters

Fuzzy C-means (FCM) clustering algorithm is a widely used method in data mining. However, there is a big limitation that the predefined number of clustering must be given. So it is very important to find an optimal number of clusters. Therefore, a new validity function of FCM clustering algorithm is proposed to verify the validity of the clustering results. This function is defined based on the intra-class compactness and inter-class separation from the fuzzy membership matrix, the data similarity between classes and the geometric structure of the data set, whose minimum value represents the optimal clustering partition result. The proposed clustering validity function and seven traditional clustering validity functions are experimentally verified on four artificial data sets and six UCI data sets. The simulation results show that the proposed validity function can obtain the optimal clustering number of the data set more accurately, and can still find the more accurate clustering number under the condition of changing the fuzzy weighted index, which has strong adaptability and robustness.

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Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405616666210104111218 ◽

2021 ◽

Vol 16 ◽

Author(s):

Ghazanfar Latif ◽

Jaafar Alghazo ◽

Fadi N. Sibai ◽

D.N.F. Awang Iskandar ◽

Adil H. Khan

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Brain Mri ◽

Tumor Segmentation ◽

Brain Anatomy ◽

Mri Segmentation ◽

Brain Tumor Segmentation ◽

Brain Images ◽

Low Contrast ◽

Fuzzy C Means

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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