Rough intuitionistic type-2 fuzzy c-means clustering algorithm for MR image segmentation

In recent years, the clinical application of magnetic resonance (MR) images is more and more extensive and in-depth. However, image segmentation is a bottleneck to restrict the application of MR imaging in clinic, and the segmentation of brain MR images now is confronted with the presence of uncertainty and noise, and various kinds of algorithms have been proposed to handle this problem. In this study, a hybrid clustering algorithm combined with a new intuitionistic fuzzy factor and local spatial information is proposed, where type-2 fuzzy logic can handle randomness, the rough set can deal with vagueness, and the intuitionistic fuzzy logic can address the external noises. Finally, the experimental tests have been done to demonstrate the superiority of the proposed technique.

• Publication year

  2019

• Venue

  IET Image Processing

• Publication date

  2019-03-01

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1049/IET-IPR.2018.5597
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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

  2016cited by this paper
• Rough Possibilistic Type-2 Fuzzy C-Means clustering for MR brain image segmentation

  2016cited by this paper
• Segmentation of brain MR images using rough set based intuitionistic fuzzy clustering

  2016cited by this paper
• MRI CRITERIA FOR THE DIAGNOSIS OF MULTIPLE SCLEROSIS: MAGNIMS CONSENSUS GUIDELINES

  2016cited by this paper
• Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

  2015cited by this paper
• Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control

  2015cited by this paper
• A review on interval type-2 fuzzy logic applications in intelligent control

  2014cited by this paper
• Brain atrophy and disability progression in multiple sclerosis patients: a 10-year follow-up study

  2014cited by this paper
• Robust level set image segmentation via a local correntropy-based K-means clustering

  2014cited by this paper
• Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation

  2014cited by this paper
• Robust spatially constrained fuzzy c-means algorithm for brain MR image segmentation

  2014influential reference
• Granular Computing: Analysis and Design of Intelligent Systems

  2013cited by this paper
• Interactive image segmentation based on synthetic graph coordinates

  2013cited by this paper
• Relapse duration, treatment intensity, and brain tissue loss in schizophrenia: a prospective longitudinal MRI study.

  2013cited by this paper
• Information granules and their use in schemes of knowledge management

  2011cited by this paper
• Intuitionistic fuzzy C-means clustering algorithms

  2010cited by this paper
• The design of granular classifiers: A study in the synergy of interval calculus and fuzzy sets in pattern recognition

  2008cited by this paper
• A Structural MRI Study of Human Brain Development from Birth to 2 Years

  2008cited by this paper
• An efficient centroid type-reduction strategy for general type-2 fuzzy logic system

  2008cited by this paper
• Rough Set Based Generalized Fuzzy $C$ -Means Algorithm and Quantitative Indices

  2007cited by this paper
• Fuzzy c-means clustering with spatial information for image segmentation

  2006cited by this paper
• New similarity measures between intuitionistic fuzzy sets and between elements

  2005cited by this paper
• Interval Set Clustering of Web Users with Rough K-Means

  2004cited by this paper
• Normalized cuts and image segmentation

  1997cited by this paper
• New similarity measures on fuzzy sets and on elements

  1997cited by this paper
• The possibilistic C-means algorithm: insights and recommendations

  1996cited by this paper
• Similarity measure between fuzzy sets and between elements

  1994cited by this paper
• A possibilistic approach to clustering

  1993cited by this paper
• The concept of a linguistic variable and its application to approximate reasoning - I

  1975cited by this paper

• Discharge Ultraviolet Image Characteristics and Insulation State Evaluation of Polluted and Wet Suspension Porcelain Insulator Strings

  2024cites this paper
• The application of FCM-based computer image segmentation technology in agricultural production

  2024cites this paper
• Critical Study of Type-2 Fuzzy Logic Control from Theory to Applications: A State-of-the-Art Comprehensive Survey

  2024cites this paper
• Distributed 3D Interior Design System Based on Fuzzy Clustering Algorithm

  2023cites this paper
• Decision-Theoretic Rough Sets based automated scheme for object and background classification in unevenly illuminated images

  2022cites this paper
• Density-based IFCM along with its interval valued and probabilistic extensions, and a review of intuitionistic fuzzy clustering methods

  2022cites this paper
• Research on Automatic Location of Seed Points in Ultrasound Breast Tumor Images Based on Fuzzy Logic Algorithm

  2021cites this paper
• Model-free Adaptive Control Design for Chaotic Systems Via Type-2 Recurrent Wavelet Fuzzy Brain Emotional Learning Networks

  2021cites this paper
• Emotion Recognition Based on Type-2 Recurrent Wavelet Fuzzy Brain Emotion Learning Network Model

  2021cites this paper
• Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation

  2021cites this paper
• Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation

  2021cites this paper
• Computer Vision and Image Processing: 4th International Conference, CVIP 2019, Jaipur, India, September 27–29, 2019, Revised Selected Papers, Part I

  2020cites this paper
• Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

  2020cites this paper
• Model-Free Adaptive Sliding Mode Robust Control with Neural Network Estimator for the Multi-Degree-of-Freedom Robotic Exoskeleton

  2020cites this paper
• Infant brain segmentation based on a combination of VGG-16 and U-Net deep neural networks

  2020cites this paper
• Retraction Retracted:Model-FreeAdaptiveSlidingModeRobustControlwith Neural Network Estimator for the Multi-Degree-of-Freedom Robotic Exoskeleton

  2020cites this paper
• A Deep Convolutional Neural Network With Fuzzy Rough Sets for FER

  2020cites this paper
• A New Fuzzy Clustering Algorithm by Incorporating Constrained Class Uncertainty-Based Entropy for Brain MR Image Segmentation

  2019cites this paper